diff --git a/src/MetaWinAnalysis.py b/src/MetaWinAnalysis.py
index cbb4295..f6ab1d7 100644
--- a/src/MetaWinAnalysis.py
+++ b/src/MetaWinAnalysis.py
@@ -1798,62 +1798,56 @@ def do_meta_analysis(data, options, decimal_places: int = 4, alpha: float = 0.05
     output, all_citations = options.report_choices()
     output_blocks.extend(output)
     if options.structure == SIMPLE_MA:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.simple_meta_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == GROUPED_MA:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.grouped_meta_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == CUMULATIVE_MA:
-        output, figure, fig_caption, chart_data = MetaWinAnalysisFunctions.cumulative_meta_analysis(data, options,
-                                                                                                    decimal_places,
-                                                                                                    alpha, norm_ci)
+        output, figure, chart_data = MetaWinAnalysisFunctions.cumulative_meta_analysis(data, options,
+                                                                                       decimal_places, alpha, norm_ci)
         analysis_values = None
         citations = []
     elif options.structure == REGRESSION_MA:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.regression_meta_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == COMPLEX_MA:
         output, analysis_values, citations = MetaWinAnalysisFunctions.complex_meta_analysis(data, options,
                                                                                             decimal_places, alpha,
                                                                                             norm_ci)
         figure = None
-        fig_caption = None
         chart_data = None
     elif options.structure == NESTED_MA:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.nested_meta_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == TRIM_FILL:
-        (output, figure, fig_caption, chart_data, analysis_values,
+        (output, figure, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.trim_and_fill_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == JACKKNIFE:
-        (output, figure, fig_caption,
-         chart_data, citations) = MetaWinAnalysisFunctions.jackknife_meta_analysis(data, options, decimal_places,
-                                                                                   alpha, norm_ci)
+        (output, figure,  chart_data,
+         citations) = MetaWinAnalysisFunctions.jackknife_meta_analysis(data, options, decimal_places, alpha, norm_ci)
         analysis_values = None
     elif options.structure == PHYLOGENETIC_MA:
         output, citations = MetaWinAnalysisFunctions.phylogenetic_meta_analysis(data, options, tree, decimal_places,
                                                                                 alpha, norm_ci)
         analysis_values = None
         figure = None
-        fig_caption = None
         chart_data = None
     elif options.structure == RANKCOR:
         output, citations = MetaWinAnalysisFunctions.rank_correlation_analysis(data, options, decimal_places)
         figure = None
-        fig_caption = None
         chart_data = None
         analysis_values = None
     else:
         output = []
         analysis_values = None
         figure = None
-        fig_caption = None
         chart_data = None
         citations = []
     all_citations.extend(citations)
     output_blocks.extend(output)
     output_blocks.extend(create_reference_list(all_citations))
-    return output_blocks, figure, fig_caption, chart_data, analysis_values
+    return output_blocks, figure, chart_data, analysis_values
 
 
 def meta_analysis(sender, data, last_effect, last_var, decimal_places: int = 4, alpha: float = 0.05,
@@ -1909,12 +1903,9 @@ def meta_analysis(sender, data, last_effect, last_var, decimal_places: int = 4,
                 meta_analysis_options.structure = None
 
         if meta_analysis_options.structure is not None:
-            output, figure, fig_caption, chart_data, _ = do_meta_analysis(data, meta_analysis_options, decimal_places,
-                                                                          alpha, tree, norm_ci)
+            output, figure, chart_data, _ = do_meta_analysis(data, meta_analysis_options, decimal_places, alpha, tree,
+                                                             norm_ci)
             sender.last_effect = meta_analysis_options.effect_data
             sender.last_var = meta_analysis_options.effect_vars
-            return output, figure, fig_caption, chart_data
-        else:
-            return None, None, None, None
-    else:
-        return None, None, None, None
+            return output, figure, chart_data
+    return None, None, None
diff --git a/src/MetaWinAnalysisFunctions.py b/src/MetaWinAnalysisFunctions.py
index 741c4d7..5982f53 100644
--- a/src/MetaWinAnalysisFunctions.py
+++ b/src/MetaWinAnalysisFunctions.py
@@ -13,8 +13,8 @@
 
 import MetaWinConstants
 from MetaWinConstants import mean_data_tuple
-from MetaWinUtils import create_output_table, inline_float, interval_to_str, create_reference_list, get_citation, \
-    exponential_label, prob_z_score
+from MetaWinUtils import create_output_table, inline_float, interval_to_str, get_citation, exponential_label, \
+    prob_z_score
 import MetaWinCharts
 from MetaWinLanguage import get_text
 
@@ -525,12 +525,12 @@ def output_filtered_bad(filtered: list, bad_data: list) -> list:
     return output_blocks
 
 
-def caption_bootstrap_text(bs_n: int):
-    """
-    extra figure caption info for forest plots with bootstrap confidence intervals
-    """
-    citation = "Adams_et_1997"
-    return get_text("bootstrap_caption").format(bs_n, get_citation(citation)), [citation]
+# def caption_bootstrap_text(bs_n: int):
+#     """
+#     extra figure caption info for forest plots with bootstrap confidence intervals
+#     """
+#     citation = "Adams_et_1997"
+#     return get_text("bootstrap_caption").format(bs_n, get_citation(citation)), [citation]
 
 # ---- I'm not convinced this is correct, which is why I'm removing it for now ---
 # def calc_aic(q: float, n: int, p: int) -> float:
@@ -577,7 +577,7 @@ def simple_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
     output_blocks = output_filtered_bad(filtered, bad_data)
 
     figure = None
-    fig_caption = None
+    # fig_caption = None
     chart_data = None
     n = len(e_data)
     citations = []
@@ -639,21 +639,22 @@ def simple_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
         citations.extend(new_cites)
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot(effect_sizes.label, forest_data, alpha,
-                                                                 options.bootstrap_mean)
-            fig_caption = get_text("Forest plot of individual effect sizes for each study, as well as the overall "
-                                   "mean.") + MetaWinCharts.caption_forest_plot_text(effect_sizes.label, alpha)
-            if options.bootstrap_mean is not None:
-                new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
-                fig_caption += new_text + create_reference_list(new_cites, True)
+            figure, chart_data = MetaWinCharts.chart_forest_plot("basic analysis", effect_sizes.label, forest_data,
+                                                                 alpha, options.bootstrap_mean)
+            # fig_caption = get_text("Forest plot of individual effect sizes for each study, as well as the overall "
+            #                        "mean.") + MetaWinCharts.common_forest_plot_caption(effect_sizes.label, alpha)
+            # if options.bootstrap_mean is not None:
+            #     new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
+            #     # fig_caption += new_text + create_reference_list(new_cites, True)
 
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
         qt, df, p, pooled_var, i2 = None, None, None, None, None
         mean_data = None
 
-    return (output_blocks, figure, fig_caption, chart_data, simple_ma_values(mean_data, pooled_var, qt, df, p, i2),
-            citations)
+    # return (output_blocks, figure, fig_caption, chart_data, simple_ma_values(mean_data, pooled_var, qt, df, p, i2),
+    #         citations)
+    return output_blocks, figure, chart_data, simple_ma_values(mean_data, pooled_var, qt, df, p, i2), citations
 
 
 # ---------- grouped meta-analysis ----------
@@ -716,7 +717,7 @@ def grouped_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
     output_blocks = output_filtered_bad(filtered, bad_data)
 
     figure = None
-    fig_caption = None
+    # fig_caption = None
     chart_data = None
     n = len(e_data)
     g_cnt = len(group_names)
@@ -863,13 +864,13 @@ def grouped_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
         citations.extend(new_cites)
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot(effect_sizes.label, forest_data, alpha,
-                                                                 options.bootstrap_mean)
-            fig_caption = get_text("group_forest_plot").format(groups.label) + \
-                MetaWinCharts.caption_forest_plot_text(effect_sizes.label, alpha)
-            if options.bootstrap_mean is not None:
-                new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
-                fig_caption += new_text + create_reference_list(new_cites, True)
+            figure, chart_data = MetaWinCharts.chart_forest_plot("grouped analysis", effect_sizes.label, forest_data,
+                                                                 alpha, options.bootstrap_mean, groups.label)
+            # fig_caption = get_text("group_forest_plot").format(groups.label) + \
+            #     MetaWinCharts.common_forest_plot_caption(effect_sizes.label, alpha)
+            # if options.bootstrap_mean is not None:
+            #     new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
+            #     fig_caption += new_text + create_reference_list(new_cites, True)
 
         global_values = simple_ma_values(global_mean_data, pooled_var, qt, df, pqt, i2)
     else:
@@ -879,8 +880,10 @@ def grouped_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
         model_het = None
         error_het = None
 
-    return (output_blocks, figure, fig_caption, chart_data,
+    return (output_blocks, figure, chart_data,
             group_ma_values(global_values, group_mean_values, group_het_values, model_het, error_het), citations)
+    # return (output_blocks, figure, fig_caption, chart_data,
+    #         group_ma_values(global_values, group_mean_values, group_het_values, model_het, error_het), citations)
 
 
 # ---------- cumulative meta-analysis ----------
@@ -907,7 +910,7 @@ def cumulative_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
     output_blocks = output_filtered_bad(filtered, bad_data)
 
     figure = None
-    fig_caption = None
+    # fig_caption = None
     chart_data = None
     n = len(tmp_data)
     if n > 1:
@@ -970,17 +973,19 @@ def cumulative_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
                              decimal_places, alpha, options.log_transformed))
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot(effect_sizes.label, cumulative_means, alpha,
-                                                     options.bootstrap_mean)
-            fig_caption = get_text("cumulative_forest_plot").format(order.label) + \
-                MetaWinCharts.caption_forest_plot_text(effect_sizes.label, alpha)
-            if options.bootstrap_mean is not None:
-                new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
-                fig_caption += new_text + create_reference_list(new_cites, True)
+            figure, chart_data = MetaWinCharts.chart_forest_plot("cumulative analysis", effect_sizes.label,
+                                                                 cumulative_means, alpha, options.bootstrap_mean,
+                                                                 order.label)
+            # fig_caption = get_text("cumulative_forest_plot").format(order.label) + \
+            #     MetaWinCharts.common_forest_plot_caption(effect_sizes.label, alpha)
+            # if options.bootstrap_mean is not None:
+            #     new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
+            #     fig_caption += new_text + create_reference_list(new_cites, True)
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
 
-    return output_blocks, figure, fig_caption, chart_data
+    return output_blocks, figure, chart_data
+    # return output_blocks, figure, fig_caption, chart_data
 
 
 # ---------- simple regression meta-analysis ----------
@@ -1033,7 +1038,7 @@ def regression_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
     output_blocks = output_filtered_bad(filtered, bad_data)
 
     figure = None
-    fig_caption = None
+    # fig_caption = None
     chart_data = None
     model_het = None
     error_het = None
@@ -1132,8 +1137,6 @@ def regression_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
         citations.extend(new_cites)
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_regression(options.independent_variable.label, effect_sizes.label,
-                                                                x_data, e_data, b1_slope, b0_intercept)
             if options.random_effects:
                 ref_list = "{}, {}, and {}".format(get_citation("Hedges_Olkin_1985"),
                                                    get_citation("Greenland_1987"),
@@ -1146,16 +1149,19 @@ def regression_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
                                               get_citation("Greenland_1987"))
                 model = get_text("fixed effects")
                 fig_citations = ["Hedges_Olkin_1985", "Greenland_1987"]
-
-            fig_caption = get_text("regression_caption").format(effect_sizes.label, options.independent_variable.label,
-                                                                model, ref_list) + \
-                create_reference_list(fig_citations, True)
+            figure, chart_data = MetaWinCharts.chart_regression(options.independent_variable.label, effect_sizes.label,
+                                                                x_data, e_data, b1_slope, b0_intercept, model,
+                                                                ref_list, fig_citations)
+            # fig_caption = get_text("regression_caption").format(effect_sizes.label, options.independent_variable.label,
+            #                                                     model, ref_list) + \
+            #     create_reference_list(fig_citations, True)
 
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
 
-    return (output_blocks, figure, fig_caption, chart_data,
-            reg_ma_values(global_values, model_het, error_het, predictors), citations)
+    return output_blocks, figure, chart_data, reg_ma_values(global_values, model_het, error_het, predictors), citations
+    # return (output_blocks, figure, fig_caption, chart_data,
+    #         reg_ma_values(global_values, model_het, error_het, predictors), citations)
 
 
 # ---------- complex (glm) meta-analysis ----------
@@ -1580,7 +1586,7 @@ def nested_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
     n = len(e_data)
 
     figure = None
-    fig_caption = None
+    # fig_caption = None
     chart_data = None
     group_het_values = None
     group_mean_values = None
@@ -1702,16 +1708,18 @@ def nested_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
         citations.extend(new_cites)
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot(effect_sizes.label, forest_data, alpha,
-                                                     options.bootstrap_mean)
-            fig_caption = get_text("nest_caption") + MetaWinCharts.caption_forest_plot_text(effect_sizes.label, alpha)
-            if options.bootstrap_mean is not None:
-                new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
-                fig_caption += new_text + create_reference_list(new_cites, True)
+            figure, chart_data = MetaWinCharts.chart_forest_plot("nested analysis", effect_sizes.label, forest_data,
+                                                                 alpha, options.bootstrap_mean)
+            # fig_caption = get_text("nest_caption") + MetaWinCharts.common_forest_plot_caption(effect_sizes.label, alpha)
+            # if options.bootstrap_mean is not None:
+            #     new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
+            #     fig_caption += new_text + create_reference_list(new_cites, True)
 
-    return (output_blocks, figure, fig_caption, chart_data,
-            group_ma_values(global_values, group_mean_values, group_het_values, model_het_values, error_het_values),
-            citations)
+    return (output_blocks, figure, chart_data, group_ma_values(global_values, group_mean_values, group_het_values,
+                                                               model_het_values, error_het_values), citations)
+    # return (output_blocks, figure, fig_caption, chart_data,
+    #         group_ma_values(global_values, group_mean_values, group_het_values, model_het_values, error_het_values),
+    #         citations)
 
 
 # ---------- trim-and-fill analysis ----------
@@ -1745,7 +1753,7 @@ def trim_and_fill_analysis(data, options, decimal_places: int = 4, alpha: float
     output_blocks = output_filtered_bad(filtered, bad_data)
 
     figure = None
-    fig_caption = None
+    # fig_caption = None
     chart_data = None
     n = len(e_data)
     citations = []
@@ -1867,16 +1875,16 @@ def trim_and_fill_analysis(data, options, decimal_places: int = 4, alpha: float
                              decimal_places, alpha, options.log_transformed))
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_trim_fill_plot(effect_sizes.label, tmp_data, n, original_mean,
+            figure, chart_data = MetaWinCharts.chart_trim_fill_plot(effect_sizes.label, tmp_data, n,  original_mean,
                                                                     mean_e)
-            fig_caption = get_text("trim_fill_caption").format(effect_sizes.label, "Duval and Tweedie 2000a, b")
-            new_cites = ["Duval_Tweedie_2000a", "Duval_Tweedie_2000b"]
-            fig_caption += create_reference_list(new_cites, True)
+            # fig_caption = get_text("trim_fill_caption").format(effect_sizes.label, "Duval and Tweedie 2000a, b")
+            # new_cites = ["Duval_Tweedie_2000a", "Duval_Tweedie_2000b"]
+            # fig_caption += create_reference_list(new_cites, True)
 
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
 
-    return output_blocks, figure, fig_caption, chart_data, None, citations
+    return output_blocks, figure, chart_data, None, citations
 
 
 # ---------- phylogenetic meta-analysis ----------
@@ -2191,7 +2199,7 @@ def jackknife_meta_analysis(data, options, decimal_places: int = 4, alpha: float
     output_blocks = output_filtered_bad(filtered, bad_data)
 
     figure = None
-    fig_caption = None
+    # fig_caption = None
     chart_data = None
     n = len(e_data)
     citations = []
@@ -2286,17 +2294,17 @@ def jackknife_meta_analysis(data, options, decimal_places: int = 4, alpha: float
         citations.extend(new_cites)
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot(effect_sizes.label, forest_data, alpha,
-                                                                 options.bootstrap_mean)
-            fig_caption = get_text("jackknife_forest_plot") + \
-                MetaWinCharts.caption_forest_plot_text(effect_sizes.label, alpha)
-            if options.bootstrap_mean is not None:
-                new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
-                fig_caption += new_text + create_reference_list(new_cites, True)
+            figure, chart_data = MetaWinCharts.chart_forest_plot("jackknife analysis", effect_sizes.label, forest_data,
+                                                                 alpha, options.bootstrap_mean)
+            # fig_caption = get_text("jackknife_forest_plot") + \
+            #     MetaWinCharts.common_forest_plot_caption(effect_sizes.label, alpha)
+            # if options.bootstrap_mean is not None:
+            #     new_text, new_cites = caption_bootstrap_text(options.bootstrap_mean)
+            #     fig_caption += new_text + create_reference_list(new_cites, True)
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
 
-    return output_blocks, figure, fig_caption, chart_data, citations
+    return output_blocks, figure, chart_data, citations
 
 
 # ---------- rank correlation analysis ----------
diff --git a/src/MetaWinCharts.py b/src/MetaWinCharts.py
index 10c51ad..d95c02a 100644
--- a/src/MetaWinCharts.py
+++ b/src/MetaWinCharts.py
@@ -11,11 +11,16 @@
 import numpy
 import scipy.stats
 
-from MetaWinUtils import exponential_label
+from MetaWinUtils import exponential_label, get_citation, create_reference_list
 from MetaWinLanguage import get_text
 import MetaWinWidgets
 
 
+# weighting options for the histograms
+WEIGHT_NONE = 0
+WEIGHT_INVVAR = 1
+WEIGHT_N = 2
+
 LINE_STYLES = ("solid", "dashed", "dotted", "dashdot")
 # MARKER_STYLES = {"point": ".", "circle": "o", "downward triangle": "v", "upward triangle": "^",
 #                  "left triangle": "<", "right triangle": ">", "downard tri": "1", "upward tri": "2", "left tri": "3",
@@ -35,6 +40,7 @@
                  "centered right caret": 9}
 
 
+# ---------- Chart Data Classes ---------- #
 class BaseChartData:
     def __init__(self):
         self.name = ""
@@ -383,19 +389,200 @@ def create_edit_panel(self):
         return self.edit_panel
 
 
+# ---------- Chart Caption Classes ---------- #
+class NormalQuantileCaption:
+    def __init__(self):
+        self.upper_limit = None
+        self.lower_limit = None
+        self.horizontal_mean = None
+        self.vertical_mean = None
+        self.regression = None
+        self.regression_scatter = None
+
+    def __str__(self):
+        "Normal Quantile plot following {}. The "
+        "standardized effect size is the effect size divided by the "
+        "square-root of its variance. The solid line represents the "
+        "regression and the dashed lines the 95% prediction envelope."
+
+        return get_text("normal_quantile_caption").format(get_citation("Wang_and_Bushman_1998")) + \
+               create_reference_list(["Wang_and_Bushman_1998"], True)
+
+
+class ScatterCaption:
+    def __init__(self):
+        self.x_label = ""
+        self.y_label = ""
+
+    def __str__(self):
+        return get_text("Scatter plot of {} vs. {}.").format(self.y_label, self.x_label)
+
+
+class HistogramCaption:
+    def __init__(self):
+        self.e_label = ""
+        self.weight_type = WEIGHT_NONE
+
+    def __str__(self):
+        caption = get_text("Histogram of {} from individual studies.").format(self.e_label)
+        if self.weight_type == WEIGHT_INVVAR:
+            caption += get_text(" Counts were weighted by the inverse of the variance of each effect size.")
+        elif self.weight_type == WEIGHT_N:
+            caption += get_text(" Counts were weighted by a sample size associated with each effect size.")
+        return caption
+
+
+class RadialCaption:
+    def __init__(self):
+        self.e_label = ""
+
+    def __str__(self):
+        return get_text("Radial_chart_caption").format(self.e_label)
+
+
+class ForestPlotBaseCaption:
+    def __init__(self):
+        self.e_label = ""
+        self.alpha = 0.05
+        self.bootstrap_n = None
+
+
+class ForestPlotCaption(ForestPlotBaseCaption):
+    def __str__(self):
+        return get_text("Forest plot of individual effect sizes for each study.") + \
+               common_forest_plot_caption(self.e_label, self.alpha, inc_median=False)
+
+
+class RegressionCaption:
+    def __init__(self):
+        self.e_label = ""
+        self.i_label = ""
+        self.model = ""
+        self.ref_list = ""
+        self.citations = []
+
+    def __str__(self):
+        return get_text("regression_caption").format(self.e_label, self.i_label, self.model, self.ref_list) + \
+                create_reference_list(self.citations, True)
+
+
+class TrimAndFillCaption:
+    def __init__(self):
+        self.e_label = ""
+        self.original_scatter = None
+        self.inferred_scatter = None
+        self.original_mean = None
+        self.inferred_mean = None
+
+    def __str__(self):
+        "Funnel plot of {} vs. precision, showing the results of a Trim and "
+        "Fill Analysis ({}). Solid black circles represent the original data; "
+        "open red circles represent inferred \"missing\" data. The dashed line "
+        "represents the mean effect size of the original data, the dotted line "
+        "the mean effect size including the inferred data."
+        new_cites = ["Duval_Tweedie_2000a", "Duval_Tweedie_2000b"]
+        return get_text("trim_fill_caption").format(self.e_label, "Duval and Tweedie 2000a, b") + \
+               create_reference_list(new_cites, True)
+
+
+class BasicAnalysisCaption(ForestPlotBaseCaption):
+    def __str__(self):
+        return get_text("Forest plot of individual effect sizes for each study, as well as the overall mean.") + \
+               common_forest_plot_caption(self.e_label, self.alpha, self.bootstrap_n)
+
+
+class GroupedAnalysisCaption(ForestPlotBaseCaption):
+    def __init__(self):
+        super().__init__()
+        self.group_label = ""
+
+    def __str__(self):
+        return get_text("group_forest_plot").format(self.group_label) + \
+               common_forest_plot_caption(self.e_label, self.alpha, self.bootstrap_n)
+
+
+class NestedAnalysisCaption(ForestPlotBaseCaption):
+    def __str__(self):
+        return get_text("nest_caption") + common_forest_plot_caption(self.e_label, self.alpha, self.bootstrap_n)
+
+
+class CumulativeAnalysisCaption(ForestPlotBaseCaption):
+    def __init__(self):
+        super().__init__()
+        self.order_label = ""
+
+    def __str__(self):
+        return get_text("cumulative_forest_plot").format(self.order_label) + \
+               common_forest_plot_caption(self.e_label, self.alpha, self.bootstrap_n)
+
+
+class JackknifeAnalysisCaption(ForestPlotBaseCaption):
+    def __str__(self):
+        return get_text("jackknife_forest_plot") + common_forest_plot_caption(self.e_label, self.alpha,
+                                                                              self.bootstrap_n)
+
+
+def common_forest_plot_caption(effect_name: str, alpha: float = 0.05, bootstrap_n: Optional[int] = None,
+                               inc_median: bool = True) -> str:
+    # " Effect size measured as {}. The dotted vertical line "
+    # "represents no effect, or a mean of zero. Circles represent "
+    # "mean effect size, with the corresponding line "
+    # "the {:0.0%} confidence interval."
+    #
+    text = get_text("forest_plot_common_caption").format(effect_name, 1 - alpha)
+    if inc_median:
+        text += get_text("forest_plot_median_caption")
+    if bootstrap_n is not None:
+        citation = "Adams_et_1997"
+        text += get_text("bootstrap_caption").format(bootstrap_n, get_citation(citation)) + \
+                create_reference_list([citation], True)
+    return text
+
+
+# ---------- Main Chart Data Class ---------- #
 class ChartData:
     """
     an object to contain all data that appears on charts
 
     this will allow chart data exporting, as well as open up the possibility of figure editing
     """
-    def __init__(self):
+    def __init__(self, caption_type):
         self.x_label = ""
         self.y_label = ""
         self.data = []
         # special adjustments
         self.suppress_y = False
         self.rescale_x = None
+        # caption
+        if caption_type == "normal quantile":
+            self.caption = NormalQuantileCaption()
+        elif caption_type == "scatter plot":
+            self.caption = ScatterCaption()
+        elif caption_type == "histogram":
+            self.caption = HistogramCaption()
+        elif caption_type == "radial":
+            self.caption = RadialCaption()
+        elif caption_type == "regression":
+            self.caption = RegressionCaption()
+        elif caption_type == "trim and fill":
+            self.caption = TrimAndFillCaption()
+        elif caption_type == "basic analysis":
+            self.caption = BasicAnalysisCaption()
+        elif caption_type == "grouped analysis":
+            self.caption = GroupedAnalysisCaption()
+        elif caption_type == "nested analysis":
+            self.caption = NestedAnalysisCaption()
+        elif caption_type == "cumulative analysis":
+            self.caption = CumulativeAnalysisCaption()
+        elif caption_type == "jackknife analysis":
+            self.caption = JackknifeAnalysisCaption()
+        elif caption_type == "forest plot":
+            self.caption = ForestPlotCaption()
+        else:
+            self.caption = ""
+
+    def caption_text(self):
+        return str(self.caption)
 
     def add_scatter(self, name: str, x_data, y_data, marker: Union[str, int] = "o", label="", zorder=0, color="#1f77b4",
                     edgecolors="#1f77b4", size=36, linewidths=1.5, linestyle="solid"):
@@ -412,6 +599,7 @@ def add_scatter(self, name: str, x_data, y_data, marker: Union[str, int] = "o",
         new_scatter.linewidths = linewidths
         new_scatter.linestyle = linestyle
         self.data.append(new_scatter)
+        return new_scatter
 
     def add_line(self, name: str, x_min, y_min, x_max, y_max, linestyle="solid", color="silver", zorder=0,
                  linewidth=1.5):
@@ -424,6 +612,7 @@ def add_line(self, name: str, x_min, y_min, x_max, y_max, linestyle="solid", col
         new_line.zorder = zorder
         new_line.linewidth = linewidth
         self.data.append(new_line)
+        return new_line
 
     def add_histogram(self, name, cnts, bins, linewidth=1, edgecolor="black", color="#1f77b4", linestyle="solid"):
         new_hist = HistogramData()
@@ -435,6 +624,7 @@ def add_histogram(self, name, cnts, bins, linewidth=1, edgecolor="black", color=
         new_hist.edgecolor = edgecolor
         new_hist.color = color
         self.data.append(new_hist)
+        return new_hist
 
     def add_arc(self, name, xc, yc, height, width, start_angle, end_angle, zorder=0, edgecolor="silver",
                 linestyle="solid", linewidth=1.5):
@@ -451,6 +641,7 @@ def add_arc(self, name, xc, yc, height, width, start_angle, end_angle, zorder=0,
         new_arc.linestyle = linestyle
         new_arc.linewidth = linewidth
         self.data.append(new_arc)
+        return new_arc
 
     def add_multi_line(self, name, x_values, y_values, linestyle="solid", color="silver", zorder=0, linewidth=1.5):
         new_ml = LineData()
@@ -462,6 +653,7 @@ def add_multi_line(self, name, x_values, y_values, linestyle="solid", color="sil
         new_ml.zorder = zorder
         new_ml.linewidth = linewidth
         self.data.append(new_ml)
+        return new_ml
 
     def add_ci(self, name, min_x, max_x, y, zorder=0, color="#1f77b4", linestyle="solid", linewidth=1.5):
         new_ci = ForestCIData()
@@ -474,6 +666,7 @@ def add_ci(self, name, min_x, max_x, y, zorder=0, color="#1f77b4", linestyle="so
         new_ci.linestyle = linestyle
         new_ci.linewidth = linewidth
         self.data.append(new_ci)
+        return new_ci
 
     def add_labels(self, name, labels, y_data):
         new_labels = LabelData()
@@ -481,6 +674,7 @@ def add_labels(self, name, labels, y_data):
         new_labels.labels = labels
         new_labels.y_pos = y_data
         self.data.append(new_labels)
+        return new_labels
 
     def add_annotations(self, name, annotations, x_data, y_data):
         new_annotation = AnnotationData()
@@ -489,6 +683,7 @@ def add_annotations(self, name, annotations, x_data, y_data):
         new_annotation.y = y_data
         new_annotation.annotations = annotations
         self.data.append(new_annotation)
+        return new_annotation
 
     def export_to_list(self):
         outlist = ["X-axis label\t{}\n".format(self.x_label),
@@ -557,9 +752,18 @@ def create_figure(chart_data):
     return figure_canvas
 
 
-def chart_forest_plot(effect_name, forest_data, alpha: float = 0.05,
-                      bootstrap_n: Optional[int] = None) -> Tuple[FigureCanvasQTAgg, ChartData]:
-    chart_data = ChartData()
+def chart_forest_plot(analysis_type: str, effect_name, forest_data, alpha: float = 0.05,
+                      bootstrap_n: Optional[int] = None,
+                      extra_name: Optional[str] = None) -> Tuple[FigureCanvasQTAgg, ChartData]:
+    chart_data = ChartData(analysis_type)
+    chart_data.caption.e_label = effect_name
+    chart_data.caption.alpha = alpha
+    chart_data.caption.bootstrap_n = bootstrap_n
+    if analysis_type == "grouped analysis":
+        chart_data.caption.group_label = extra_name
+    elif analysis_type == "cumulative analysis":
+        chart_data.caption.order_label = extra_name
+
     chart_data.x_label = effect_name
     chart_data.suppress_y = True
 
@@ -619,13 +823,6 @@ def chart_forest_plot(effect_name, forest_data, alpha: float = 0.05,
     return figure_canvas, chart_data
 
 
-def caption_forest_plot_text(effect_name: str, alpha: float = 0.05, inc_median: bool = True) -> str:
-    text = get_text("forest_plot_common_caption").format(effect_name, 1 - alpha)
-    if inc_median:
-        text += get_text("forest_plot_median_caption")
-    return text
-
-
 def add_regression_to_chart(x_name: str, y_name: str, x_data, y_data, slope: float, intercept: float,
                             x_min: float, x_max: float, chart_data) -> None:
     y_at_min = slope*x_min + intercept
@@ -633,14 +830,22 @@ def add_regression_to_chart(x_name: str, y_name: str, x_data, y_data, slope: flo
 
     chart_data.x_label = x_name
     chart_data.y_label = y_name
-    chart_data.add_scatter(get_text("Point Data"), x_data, y_data, zorder=10)
-    chart_data.add_line(get_text("Regression Line"), x_min, y_at_min, x_max, y_at_max, zorder=8, color="silver")
+    chart_data.caption.regression_scatter = chart_data.add_scatter(get_text("Point Data"), x_data, y_data, zorder=10)
+    chart_data.caption.regression = chart_data.add_line(get_text("Regression Line"), x_min, y_at_min, x_max, y_at_max,
+                                                        zorder=8, color="silver")
 
 
-def chart_regression(x_name, y_name, x_data, y_data, slope, intercept) -> Tuple[FigureCanvasQTAgg, ChartData]:
+def chart_regression(x_name, y_name, x_data, y_data, slope, intercept, model, ref_list,
+                     citations) -> Tuple[FigureCanvasQTAgg, ChartData]:
     x_min = numpy.min(x_data)
     x_max = numpy.max(x_data)
-    chart_data = ChartData()
+    chart_data = ChartData("regression")
+    chart_data.caption.e_label = y_name
+    chart_data.caption.i_label = x_name
+    chart_data.caption.model = model
+    chart_data.caption.ref_list = ref_list
+    chart_data.caption.citations = citations
+
     add_regression_to_chart(x_name, y_name, x_data, y_data, slope, intercept, x_min, x_max, chart_data)
 
     figure_canvas = create_figure(chart_data)
@@ -668,21 +873,22 @@ def add_quantile_axes_to_chart(x_data, y_data, slope: float, intercept: float, c
     y_lower = [y_pos[i] - t_score*math.sqrt(mse*(1 + 1/n + ((x_pos[i] - x_mean)**2)/ss_x)) for i in range(nsteps)]
     y_upper = [y_pos[i] + t_score*math.sqrt(mse*(1 + 1/n + ((x_pos[i] - x_mean)**2)/ss_x)) for i in range(nsteps)]
 
-    chart_data.add_multi_line(get_text("Lower Prediction Limit"), x_pos, y_lower, linestyle="dashed", color="silver",
-                              zorder=3)
-    chart_data.add_multi_line(get_text("Upper Prediction Limit"), x_pos, y_upper, linestyle="dashed", color="silver",
-                              zorder=3)
+    chart_data.caption.lower_limit = chart_data.add_multi_line(get_text("Lower Prediction Limit"), x_pos, y_lower,
+                                                               linestyle="dashed", color="silver", zorder=3)
+    chart_data.caption.upper_limit = chart_data.add_multi_line(get_text("Upper Prediction Limit"), x_pos, y_upper,
+                                                               linestyle="dashed", color="silver", zorder=3)
 
     # draw center lines
-    chart_data.add_line(get_text("Horizontal Axis Mean Line"), 0, min(y_min, min(y_lower)), 0, max(y_max, max(y_upper)),
-                        linestyle="dotted", color="silver")
-    chart_data.add_line(get_text("Vertical Axis Mean Line"), x_min, y_mean, x_max, y_mean, linestyle="dotted",
-                        color="silver")
+    chart_data.caption.horizontal_mean = chart_data.add_line(get_text("Horizontal Axis Mean Line"), 0,
+                                                             min(y_min, min(y_lower)), 0, max(y_max, max(y_upper)),
+                                                             linestyle="dotted", color="silver")
+    chart_data.caption.vertical_mean = chart_data.add_line(get_text("Vertical Axis Mean Line"), x_min, y_mean, x_max,
+                                                           y_mean, linestyle="dotted", color="silver")
 
 
 def chart_normal_quantile(x_name, y_name, x_data, y_data, slope, intercept,
                           alpha: float = 0.05) -> Tuple[FigureCanvasQTAgg, ChartData]:
-    chart_data = ChartData()
+    chart_data = ChartData("normal quantile")
     add_quantile_axes_to_chart(x_data, y_data, slope, intercept, chart_data, alpha)
     x_min = numpy.min(x_data)
     x_max = numpy.max(x_data)
@@ -769,7 +975,8 @@ def add_radial_curve_to_chart(effect_label: str, r: float, min_e: float, max_e:
 
 def chart_radial(e_name, x_data, y_data, slope, min_e, max_e, is_log: bool = False) -> Tuple[FigureCanvasQTAgg,
                                                                                              ChartData]:
-    chart_data = ChartData()
+    chart_data = ChartData("radial")
+    chart_data.caption.e_label = e_name
     max_d = numpy.max(numpy.sqrt(numpy.square(x_data) + numpy.square(y_data)))+1
     x_min = 0
     x_max = (max_d + 1)*math.cos(math.atan(slope))
@@ -782,15 +989,25 @@ def chart_radial(e_name, x_data, y_data, slope, min_e, max_e, is_log: bool = Fal
     return figure_canvas, chart_data
 
 
-def chart_histogram(e_data, w_data, n_bins, e_label, weighted: bool = False) -> Tuple[FigureCanvasQTAgg, ChartData]:
-    if weighted:
-        cnts, bins = numpy.histogram(e_data, n_bins, weights=w_data)
-        y_label = get_text("Weighted Count")
-    else:
+def chart_histogram(e_data, w_data, n_bins, e_label,
+                    weighted: int = WEIGHT_NONE) -> Tuple[FigureCanvasQTAgg, ChartData]:
+    if weighted == WEIGHT_NONE:
         cnts, bins = numpy.histogram(e_data, n_bins)
         y_label = get_text("Count")
+    else:
+        cnts, bins = numpy.histogram(e_data, n_bins, weights=w_data)
+        y_label = get_text("Weighted Count")
+    # if weighted:
+    #     cnts, bins = numpy.histogram(e_data, n_bins, weights=w_data)
+    #     y_label = get_text("Weighted Count")
+    # else:
+    #     cnts, bins = numpy.histogram(e_data, n_bins)
+    #     y_label = get_text("Count")
+
+    chart_data = ChartData("histogram")
+    chart_data.caption.e_label = e_label
+    chart_data.caption.weight_type = weighted
 
-    chart_data = ChartData()
     chart_data.x_label = e_label
     chart_data.y_label = y_label
     chart_data.add_histogram("Bin Counts", cnts, bins, edgecolor="black", linewidth=1)
@@ -893,7 +1110,9 @@ def chart_phylogeny(root) -> FigureCanvasQTAgg:
 
 
 def chart_scatter(x_data, y_data, x_label: str = "x", y_label: str = "y") -> Tuple[FigureCanvasQTAgg, ChartData]:
-    chart_data = ChartData()
+    chart_data = ChartData("scatter plot")
+    chart_data.caption.x_label = x_label
+    chart_data.caption.y_label = y_label
     chart_data.x_label = x_label
     chart_data.y_label = y_label
     chart_data.add_scatter(get_text("Point Data"), x_data, y_data)
@@ -903,27 +1122,31 @@ def chart_scatter(x_data, y_data, x_label: str = "x", y_label: str = "y") -> Tup
 
 
 def chart_trim_fill_plot(effect_label, data, n, original_mean, new_mean) -> Tuple[FigureCanvasQTAgg, ChartData]:
-    chart_data = ChartData()
+    chart_data = ChartData("trim and fill")
+    chart_data.caption.e_label = effect_label
     chart_data.x_label = effect_label
     chart_data.y_label = "{} (1/SE)".format(get_text("Precision"))
 
     # plot original points
     x_data = data[:n, 0]
     y_data = numpy.reciprocal(numpy.sqrt(data[:n, 2]))
-    chart_data.add_scatter(get_text("Original Data"), x_data, y_data, color="black", edgecolors="black")
+    chart_data.caption.original_scatter = chart_data.add_scatter(get_text("Original Data"), x_data, y_data,
+                                                                 color="black", edgecolors="black")
     y_min = numpy.min(y_data)
     y_max = numpy.max(y_data)
 
     # plot inferred points
     x_data = data[n:, 0]
     y_data = numpy.reciprocal(numpy.sqrt(data[n:, 2]))
-    chart_data.add_scatter(get_text("Inferred Data"), x_data, y_data, edgecolors="red", color="none")
+    chart_data.caption.inferred_scatter = chart_data.add_scatter(get_text("Inferred Data"), x_data, y_data,
+                                                                 edgecolors="red", color="none")
 
     # draw original and new mean
-    chart_data.add_line(get_text("Original Mean"), original_mean, y_min, original_mean, y_max, color="silver",
-                        linestyle="dashed", zorder=1)
-    chart_data.add_line(get_text("Inferred Mean"), new_mean, y_min, new_mean, y_max, color="red", linestyle="dashed",
-                        zorder=1)
+    chart_data.caption.original_mean = chart_data.add_line(get_text("Original Mean"), original_mean, y_min,
+                                                           original_mean, y_max, color="silver", linestyle="dashed",
+                                                           zorder=1)
+    chart_data.caption.inferred_mean = chart_data.add_line(get_text("Inferred Mean"), new_mean, y_min, new_mean, y_max,
+                                                           color="red", linestyle="dashed", zorder=1)
 
     figure_canvas = create_figure(chart_data)
     return figure_canvas, chart_data
diff --git a/src/MetaWinDraw.py b/src/MetaWinDraw.py
index 8e45445..bc53f10 100644
--- a/src/MetaWinDraw.py
+++ b/src/MetaWinDraw.py
@@ -22,17 +22,10 @@
 import MetaWinConstants
 from MetaWinConstants import mean_data_tuple
 from MetaWinWidgets import add_ok_cancel_help_button_layout, add_effect_choice_to_dialog
-from MetaWinUtils import create_reference_list, get_citation
 import MetaWinCharts
 from MetaWinLanguage import get_text
 
 
-# weighting options for the histograms
-WEIGHT_NONE = 0
-WEIGHT_INVVAR = 1
-WEIGHT_N = 2
-
-
 class MetaAnalysisDrawScatterDialog(QDialog):
     def __init__(self, data: MetaWinData):
         super().__init__()
@@ -378,9 +371,9 @@ def draw_scatter_plot(data, x_data_col, y_data_col):
         x_data = numpy.array(x_data)
         y_data = numpy.array(y_data)
         figure, chart_data = MetaWinCharts.chart_scatter(x_data, y_data, x_data_col.label, y_data_col.label)
-        fig_caption = get_text("Scatter plot of {} vs. {}.").format(y_data_col.label, x_data_col.label)
-        return figure, fig_caption, chart_data
-    return None, None, None
+        # fig_caption = get_text("Scatter plot of {} vs. {}.").format(y_data_col.label, x_data_col.label)
+        return figure, chart_data
+    return None, None
 
 
 def draw_scatter_dialog(sender, data):
@@ -388,12 +381,12 @@ def draw_scatter_dialog(sender, data):
     if sender.draw_dialog.exec():
         x_data_col = sender.draw_dialog.columns[sender.draw_dialog.x_box.currentIndex()]
         y_data_col = sender.draw_dialog.columns[sender.draw_dialog.y_box.currentIndex()]
-        figure, fig_caption, chart_data = draw_scatter_plot(data, x_data_col, y_data_col)
+        figure, chart_data = draw_scatter_plot(data, x_data_col, y_data_col)
         if figure is not None:
-            return figure, fig_caption, chart_data
+            return figure, chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None, None
+    return None, None
 
 
 def calculate_regression(x: numpy.array, y: numpy.array) -> Tuple[float, float]:
@@ -439,11 +432,11 @@ def draw_normal_quantile_plot(data, e_data_col, v_data_col, alpha: float = 0.05)
         figure, chart_data = MetaWinCharts.chart_normal_quantile(get_text("Normal Quantile"),
                                                                  get_text("Standardized Effect Size"),
                                                                  x_data, y_data, slope, intercept, alpha)
-        fig_caption = get_text("normal_quantile_caption").format(get_citation("Wang_and_Bushman_1998")) + \
-                      create_reference_list(["Wang_and_Bushman_1998"], True)
+        # fig_caption = get_text("normal_quantile_caption").format(get_citation("Wang_and_Bushman_1998")) + \
+        #               create_reference_list(["Wang_and_Bushman_1998"], True)
 
-        return figure, fig_caption, chart_data
-    return None, None, None
+        return figure, chart_data
+    return None, None
 
 
 def draw_normal_quantile_dialog(sender, data, last_effect, last_var, alpha: float = 0.05):
@@ -451,12 +444,12 @@ def draw_normal_quantile_dialog(sender, data, last_effect, last_var, alpha: floa
     if sender.draw_dialog.exec():
         e_data_col = sender.draw_dialog.columns[sender.draw_dialog.effect_size_box.currentIndex()]
         v_data_col = sender.draw_dialog.columns[sender.draw_dialog.variance_box.currentIndex()]
-        figure, fig_caption, chart_data = draw_normal_quantile_plot(data, e_data_col, v_data_col, alpha)
+        figure, chart_data = draw_normal_quantile_plot(data, e_data_col, v_data_col, alpha)
         if figure is not None:
-            return figure, fig_caption, chart_data
+            return figure, chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None, None
+    return None, None
 
 
 def draw_radial_plot(data, e_data_col, v_data_col, is_log: bool = False):
@@ -489,10 +482,10 @@ def draw_radial_plot(data, e_data_col, v_data_col, is_log: bool = False):
 
         figure, chart_data = MetaWinCharts.chart_radial(e_data_col.label, x_data, y_data, slope_through_origin, min_e,
                                                         max_e, is_log)
-        fig_caption = get_text("Radial_chart_caption").format(e_data_col.label)
-        fig_caption += create_reference_list(["Galbraith_1988", "Galbraith_1994"], True)
-        return figure, fig_caption, chart_data
-    return None, None, None
+        # fig_caption = get_text("Radial_chart_caption").format(e_data_col.label)
+        # fig_caption += create_reference_list(["Galbraith_1988", "Galbraith_1994"], True)
+        return figure, chart_data
+    return None, None
 
 
 def draw_radial_dialog(sender, data, last_effect, last_var):
@@ -501,12 +494,12 @@ def draw_radial_dialog(sender, data, last_effect, last_var):
         e_data_col = sender.draw_dialog.columns[sender.draw_dialog.effect_size_box.currentIndex()]
         v_data_col = sender.draw_dialog.columns[sender.draw_dialog.variance_box.currentIndex()]
         is_log = sender.draw_dialog.log_transform_box.isChecked()
-        figure, fig_caption, chart_data = draw_radial_plot(data, e_data_col, v_data_col, is_log)
+        figure, chart_data = draw_radial_plot(data, e_data_col, v_data_col, is_log)
         if figure is not None:
-            return figure, fig_caption, chart_data
+            return figure, chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None, None
+    return None, None
 
 
 def draw_histogram_plot(data, e_data_col, w_data_col, weight_type, n_bins: int):
@@ -517,10 +510,10 @@ def draw_histogram_plot(data, e_data_col, w_data_col, weight_type, n_bins: int):
     for r, row in enumerate(data.rows):
         if row.not_filtered():
             e = data.check_value(r, e_data_col.position(), value_type=MetaWinConstants.VALUE_NUMBER)
-            if weight_type != WEIGHT_NONE:
+            if weight_type != MetaWinCharts.WEIGHT_NONE:
                 w = data.check_value(r, w_data_col.position(), value_type=MetaWinConstants.VALUE_NUMBER)
                 if (e is not None) and (w is not None):
-                    if weight_type == WEIGHT_INVVAR:
+                    if weight_type == MetaWinCharts.WEIGHT_INVVAR:
                         if w > 0:
                             w_data.append(1/w)
                             e_data.append(e)
@@ -540,20 +533,9 @@ def draw_histogram_plot(data, e_data_col, w_data_col, weight_type, n_bins: int):
     if len(e_data) > 0:
         e_data = numpy.array(e_data)
         w_data = numpy.array(w_data)
-        if weight_type == WEIGHT_NONE:
-            weighted = False
-        else:
-            weighted = True
-
-        figure, chart_data = MetaWinCharts.chart_histogram(e_data, w_data, n_bins, e_data_col.label, weighted)
-        fig_caption = get_text("Histogram of {} from individual studies.").format(e_data_col.label)
-        if weight_type == WEIGHT_INVVAR:
-            fig_caption += get_text(" Counts were weighted by the inverse of the variance of each effect size.")
-        elif weight_type == WEIGHT_N:
-            fig_caption += get_text(" Counts were weighted by a sample size associated with each effect size.")
-        return figure, fig_caption, chart_data
-    else:
-        return None, None, None
+        figure, chart_data = MetaWinCharts.chart_histogram(e_data, w_data, n_bins, e_data_col.label, weight_type)
+        return figure, chart_data
+    return None, None
 
 
 def draw_histogram_dialog(sender, data, last_effect, last_var):
@@ -562,18 +544,18 @@ def draw_histogram_dialog(sender, data, last_effect, last_var):
         e_data_col = sender.draw_dialog.columns[sender.draw_dialog.effect_size_box.currentIndex()]
         w_data_col = sender.draw_dialog.columns[sender.draw_dialog.weight_box.currentIndex()]
         if sender.draw_dialog.weight_var.isChecked():
-            weight_type = WEIGHT_INVVAR
+            weight_type = MetaWinCharts.WEIGHT_INVVAR
         elif sender.draw_dialog.weight_n.isChecked():
-            weight_type = WEIGHT_N
+            weight_type = MetaWinCharts.WEIGHT_N
         else:
-            weight_type = WEIGHT_NONE
+            weight_type = MetaWinCharts.WEIGHT_NONE
         n_bins = int(sender.draw_dialog.bin_edit.text())
-        figure, fig_caption, chart_data = draw_histogram_plot(data, e_data_col, w_data_col, weight_type, n_bins)
+        figure, chart_data = draw_histogram_plot(data, e_data_col, w_data_col, weight_type, n_bins)
         if figure is not None:
-            return figure, fig_caption, chart_data
+            return figure, chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None, None
+    return None, None
 
 
 def draw_forest_plot(data, e_data_col, v_data_col, alpha: float = 0.05):
@@ -594,11 +576,11 @@ def draw_forest_plot(data, e_data_col, v_data_col, alpha: float = 0.05):
         else:
             filtered.append(row.label)
 
-    figure, chart_data = MetaWinCharts.chart_forest_plot(e_data_col.label, data_list, alpha, None)
+    figure, chart_data = MetaWinCharts.chart_forest_plot("forest plot", e_data_col.label, data_list, alpha, None)
 
-    fig_caption = get_text("Forest plot of individual effect sizes for each study.") + \
-                  MetaWinCharts.caption_forest_plot_text(e_data_col.label, alpha, inc_median=False)
-    return figure, fig_caption, chart_data
+    # fig_caption = get_text("Forest plot of individual effect sizes for each study.") + \
+    #               MetaWinCharts.common_forest_plot_caption(e_data_col.label, alpha, inc_median=False)
+    return figure, chart_data
 
 
 def draw_forest_dialog(sender, data, last_effect, last_var, alpha: float = 0.05):
@@ -606,12 +588,12 @@ def draw_forest_dialog(sender, data, last_effect, last_var, alpha: float = 0.05)
     if sender.draw_dialog.exec():
         e_data_col = sender.draw_dialog.columns[sender.draw_dialog.effect_size_box.currentIndex()]
         v_data_col = sender.draw_dialog.columns[sender.draw_dialog.variance_box.currentIndex()]
-        figure, fig_caption, chart_data = draw_forest_plot(data, e_data_col, v_data_col, alpha)
+        figure, chart_data = draw_forest_plot(data, e_data_col, v_data_col, alpha)
         if figure is not None:
-            return figure, fig_caption, chart_data
+            return figure, chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None, None
+    return None, None
 
 
 def edit_figure(sender, chart_data):
diff --git a/src/MetaWinMain.py b/src/MetaWinMain.py
index 136d774..2fde591 100644
--- a/src/MetaWinMain.py
+++ b/src/MetaWinMain.py
@@ -77,7 +77,6 @@ def __init__(self, config: dict):
         self.empty_col_num = 10
         self.empty_row_num = 15
         self.chart_data = None
-        self.chart_caption = ""
         self.init_ui()
 
     def init_ui(self):
@@ -696,18 +695,18 @@ def meta_analysis(self) -> None:
                 norm_ci = False
             else:
                 norm_ci = True
-            output, figure, fig_caption, chart_data = MetaWinAnalysis.meta_analysis(self, self.data, self.last_effect,
-                                                                                    self.last_var, self.output_decimals,
-                                                                                    self.alpha, self.phylogeny, norm_ci)
+            output, figure, chart_data = MetaWinAnalysis.meta_analysis(self, self.data, self.last_effect,
+                                                                       self.last_var, self.output_decimals,
+                                                                       self.alpha, self.phylogeny, norm_ci)
             if output is not None:
                 self.write_multi_output_blocks(output)
                 self.main_area.setCurrentIndex(1)
                 if figure is not None:
-                    self.show_figure(figure, fig_caption, chart_data)
+                    self.show_figure(figure, chart_data)
         else:
             MetaWinMessages.report_warning(self, get_text("Warning"), get_text("No data has been loaded."))
 
-    def show_figure(self, figure, fig_caption: str, chart_data) -> None:
+    def show_figure(self, figure, chart_data) -> None:
         """
         Replace the current figure in the graphics tab with a new figure, toolbar, and caption
         """
@@ -720,8 +719,7 @@ def show_figure(self, figure, fig_caption: str, chart_data) -> None:
         toolbar = NavigationToolbar2QT(figure, None)
         self.save_graph_action.triggered.connect(toolbar.save_figure)
         caption_box = QTextEdit()
-        caption_box.setText(fig_caption)
-        self.chart_caption = fig_caption
+        caption_box.setText(chart_data.caption_text())
         self.graph_layout.addWidget(figure, stretch=8)
         self.graph_layout.addWidget(caption_box, stretch=1)
         self.chart_data = chart_data
@@ -744,41 +742,39 @@ def refresh_tree_panel(self) -> None:
 
     def draw_scatter_plot(self) -> None:
         if self.data is not None:
-            figure, fig_caption, chart_data = MetaWinDraw.draw_scatter_dialog(self, self.data)
+            figure, chart_data = MetaWinDraw.draw_scatter_dialog(self, self.data)
             if figure is not None:
-                self.show_figure(figure, fig_caption, chart_data)
+                self.show_figure(figure, chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def draw_histogram(self) -> None:
         if self.data is not None:
-            figure, fig_caption, chart_data = MetaWinDraw.draw_histogram_dialog(self, self.data, self.last_effect,
-                                                                                self.last_var)
+            figure, chart_data = MetaWinDraw.draw_histogram_dialog(self, self.data, self.last_effect, self.last_var)
             if figure is not None:
-                self.show_figure(figure, fig_caption, chart_data)
+                self.show_figure(figure, chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def draw_normal_quantile_plot(self) -> None:
         if self.data is not None:
-            figure, fig_caption, chart_data = MetaWinDraw.draw_normal_quantile_dialog(self, self.data, self.last_effect,
-                                                                                      self.last_var)
+            figure, chart_data = MetaWinDraw.draw_normal_quantile_dialog(self, self.data, self.last_effect,
+                                                                         self.last_var)
             if figure is not None:
-                self.show_figure(figure, fig_caption, chart_data)
+                self.show_figure(figure, chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def draw_radial_plot(self) -> None:
         if self.data is not None:
-            figure, fig_caption, chart_data = MetaWinDraw.draw_radial_dialog(self, self.data, self.last_effect,
-                                                                             self.last_var)
+            figure, chart_data = MetaWinDraw.draw_radial_dialog(self, self.data, self.last_effect, self.last_var)
             if figure is not None:
-                self.show_figure(figure, fig_caption, chart_data)
+                self.show_figure(figure, chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def draw_forest_plot(self) -> None:
         if self.data is not None:
-            figure, fig_caption, chart_data = MetaWinDraw.draw_forest_dialog(self, self.data, self.last_effect,
-                                                                             self.last_var, self.alpha)
+            figure, chart_data = MetaWinDraw.draw_forest_dialog(self, self.data, self.last_effect, self.last_var,
+                                                                self.alpha)
             if figure is not None:
-                self.show_figure(figure, fig_caption, chart_data)
+                self.show_figure(figure, chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def clear_filters(self) -> None:
@@ -851,8 +847,7 @@ def edit_graph(self):
         if self.chart_data is not None:
             figure = MetaWinDraw.edit_figure(self, self.chart_data)
             if figure is not None:
-                caption = self.chart_caption
-                self.show_figure(figure, caption, self.chart_data)
+                self.show_figure(figure, self.chart_data)
 
     def change_conf_int_distribution(self):
         if self.confidence_interval_dist == "Normal":
diff --git a/tests/test_metawin.py b/tests/test_metawin.py
index b47364a..237f6e4 100644
--- a/tests/test_metawin.py
+++ b/tests/test_metawin.py
@@ -28,7 +28,7 @@
 import MetaWinDraw
 
 
-TEST_FIGURES = False
+TEST_FIGURES = True
 
 
 class TestFigureDialog(QDialog):
@@ -291,11 +291,11 @@ def test_simple_meta_analysis():
     options.rosenberg_failsafe = 0.05
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
     mean_values = analysis_values.mean_data
@@ -328,11 +328,11 @@ def test_simple_meta_analysis_lep():
     options.effect_vars = data.cols[5]
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
     mean_values = analysis_values.mean_data
@@ -365,11 +365,11 @@ def test_simple_meta_analysis_lep_randeff():
     options.effect_vars = data.cols[5]
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
     mean_values = analysis_values.mean_data
@@ -405,7 +405,7 @@ def test_simple_meta_analysis_random_effects():
     options.rosenthal_failsafe = 0.05
     options.rosenberg_failsafe = 0.05
 
-    output, _, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     mean_values = analysis_values.mean_data
 
@@ -443,10 +443,10 @@ def test_simple_meta_analysis_bootstrap():
     options.bootstrap_mean = 9999
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -483,11 +483,11 @@ def test_group_meta_analysis_lep_suborders():
     options.groups = data.cols[1]
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -537,11 +537,11 @@ def test_group_meta_analysis_lep_suborders_rand_eff():
     options.random_effects = True
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -618,11 +618,11 @@ def test_group_meta_analysis_lep_families():
     options.create_graph = True
     data.cols[2].group_filter = ["Yponomeutidae", "Lycaenidae", "Hesperiidae"]
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -661,10 +661,10 @@ def test_group_meta_analysis_bootstrap():
     options.bootstrap_mean = 9999
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -681,10 +681,10 @@ def test_group_meta_analysis_lrr():
 
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -713,10 +713,10 @@ def test_cumulative_meta_analysis():
 
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -732,10 +732,10 @@ def test_cumulative_meta_analysis_bootstrap():
 
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -761,11 +761,11 @@ def test_regression_meta_analysis_lep():
     options.independent_variable = data.cols[3]
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -807,11 +807,11 @@ def test_regression_meta_analysis_lep_randeff():
     options.random_effects = True
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -872,7 +872,7 @@ def test_complex_meta_analysis_lep_simple_regression():
     options.continuous_vars = [data.cols[3]]
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     global_values = analysis_values.global_values
@@ -918,7 +918,7 @@ def test_complex_meta_analysis_lep_group():
     options.continuous_vars = []
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     global_values = analysis_values.global_values
@@ -966,7 +966,7 @@ def test_complex_meta_analysis_lep():
     options.continuous_vars = [data.cols[3]]
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     global_values = analysis_values.global_values
@@ -1025,7 +1025,7 @@ def test_complex_meta_analysis_lep_randomeff():
     options.random_effects = True
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     global_values = analysis_values.global_values
@@ -1088,11 +1088,11 @@ def test_nested_meta_analysis_lep():
     options.create_graph = True
     data.cols[2].group_filter = ["Yponomeutidae", "Lycaenidae", "Hesperiidae"]
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -1200,8 +1200,8 @@ def test_scatter_plot():
     x_col = data.cols[11]
     y_col = data.cols[10]
     if TEST_FIGURES:
-        figure, fig_caption, _ = MetaWinDraw.draw_scatter_plot(data, x_col, y_col)
-        test_win = TestFigureDialog(figure, fig_caption)
+        figure, chart_data = MetaWinDraw.draw_scatter_plot(data, x_col, y_col)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1210,8 +1210,8 @@ def test_normal_quantile_plot():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, fig_caption, _ = MetaWinDraw.draw_normal_quantile_plot(data, e_col, v_col)
-        test_win = TestFigureDialog(figure, fig_caption)
+        figure, chart_data = MetaWinDraw.draw_normal_quantile_plot(data, e_col, v_col)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1220,8 +1220,8 @@ def test_radial_plot_d():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, fig_caption, _ = MetaWinDraw.draw_radial_plot(data, e_col, v_col, False)
-        test_win = TestFigureDialog(figure, fig_caption)
+        figure, chart_data = MetaWinDraw.draw_radial_plot(data, e_col, v_col, False)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1230,8 +1230,8 @@ def test_radial_plot_lnrr():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, fig_caption, _ = MetaWinDraw.draw_radial_plot(data, e_col, v_col, True)
-        test_win = TestFigureDialog(figure, fig_caption)
+        figure, chart_data = MetaWinDraw.draw_radial_plot(data, e_col, v_col, True)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1240,8 +1240,8 @@ def test_histogram_d_unweighted():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, fig_caption, _ = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 0, 10)
-        test_win = TestFigureDialog(figure, fig_caption)
+        figure, chart_data = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 0, 10)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1250,8 +1250,8 @@ def test_histogram_d_weighted_invvar():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, fig_caption, _ = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 1, 10)
-        test_win = TestFigureDialog(figure, fig_caption)
+        figure, chart_data = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 1, 10)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1260,8 +1260,8 @@ def test_histogram_d_weighted_sample_size():
     e_col = data.cols[10]
     v_col = data.cols[2]
     if TEST_FIGURES:
-        figure, fig_caption, _ = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 2, 15)
-        test_win = TestFigureDialog(figure, fig_caption)
+        figure, chart_data = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 2, 15)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1270,8 +1270,8 @@ def test_forest_plot():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, fig_caption, _ = MetaWinDraw.draw_forest_plot(data, e_col, v_col)
-        test_win = TestFigureDialog(figure, fig_caption)
+        figure, chart_data = MetaWinDraw.draw_forest_plot(data, e_col, v_col)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1296,11 +1296,11 @@ def test_trim_and_fill_analysis():
     options.create_graph = True
     options.k_estimator = "L"
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1325,11 +1325,11 @@ def test_trim_and_fill_analysis_negative_mean():
     options.create_graph = True
     options.k_estimator = "R"
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1354,11 +1354,11 @@ def test_jackknife():
     options.effect_vars = data.cols[5]
     options.create_graph = True
 
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, fig_caption)
+        test_win = TestFigureDialog(figure, chart_data.caption_text())
         test_win.exec()
 
 
@@ -1377,22 +1377,21 @@ def test_phylogenetic_simple_test():
     options.tip_names = data.cols[0]
     print("UNWEIGHTED")
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     print()
     print()
     print("WEIGHTED")
     options.effect_vars = data.cols[2]
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     print()
     print()
     print("PHYLOGENETIC")
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4,
-                                                                                                tree=tree)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
 
@@ -1409,12 +1408,11 @@ def test_phylogenetic_glm_simple():
     options.random_effects = True
 
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
     print(data.column_labels()[1])
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4,
-                                                                                                tree=tree)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1426,11 +1424,10 @@ def test_phylogenetic_glm_simple():
     options.effect_vars = data.cols[4]
 
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4,
-                                                                                                tree=tree)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1441,11 +1438,10 @@ def test_phylogenetic_glm_simple():
     options.effect_data = data.cols[5]
     options.effect_vars = data.cols[6]
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4,
-                                                                                                tree=tree)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1456,11 +1452,10 @@ def test_phylogenetic_glm_simple():
     options.effect_data = data.cols[7]
     options.effect_vars = data.cols[8]
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4,
-                                                                                                tree=tree)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1471,11 +1466,10 @@ def test_phylogenetic_glm_simple():
     options.effect_data = data.cols[9]
     options.effect_vars = data.cols[10]
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4,
-                                                                                                tree=tree)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1486,9 +1480,8 @@ def test_phylogenetic_glm_simple():
     options.effect_data = data.cols[11]
     options.effect_vars = data.cols[12]
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, fig_caption, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4,
-                                                                                                tree=tree)
+    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)