diff --git a/src/statsy/Probability.py b/src/statsy/Probability.py
index 161d24f..945a39c 100644
--- a/src/statsy/Probability.py
+++ b/src/statsy/Probability.py
@@ -1,4 +1,5 @@
 from .main import *
+from .Constants import *
 
 def simple_probability(desired_outcomes:str,total_outcomes:str)->Number:
     """
@@ -43,3 +44,113 @@ def bare_disjunctive_probability(a_desired:Number,a_possible:Number,b_desired:Nu
     P_of_b = simple_probability(b_desired,b_possible)
     jp = joint_probability(P_of_a,P_of_b)
     return P_of_a + P_of_b - jp
+
+def permutation(number_of_items:Optional[str],r:Number,data:Optional[list]=None)->Number:
+    """
+    Returns the permutation of formula nPr .\n
+    Either the number of items in the set must be given or the data itself. \n
+    """
+    if data is not None:
+        number_of_items = len(data)
+    return (math.factorial(number_of_items)) / (math.factorial(number_of_items-r))
+
+def combination(number_of_items:Optional[str],r:Number,data:Optional[list]=None)->Number:
+    """
+    Returns the permutation of formula nPr .\n
+    Either the number of items in the set must be given or the data itself. \n
+    """
+    if data is not None:
+        number_of_items = len(data)
+    return (math.factorial(number_of_items)) / (math.factorial(number_of_items-r) * math.factorial(r))
+
+def binomial_probability(number_of_items:Optional[str],p:Number,k:Number,data:Optional[list]=None)->Number:
+    """
+    Returns the binomial probability where: \n
+    number_of_items = number of events, trials, etc. \n
+    p = probability of desired outcome \n
+    k = number of sucesses \n
+    """
+    if data is not None:
+        number_of_items=len(data)
+    return combination(number_of_items,k) * (p**k) * ( (1-p)** (number_of_items-k))
+
+def binomial_distribution_mean(number_of_items:int,p:Number,data:Optional[list]=None)->Number:
+    """
+    Returns the mean of binomial distribution where N = number of items and \n
+    p = probability of desired outcome. \n 
+    """
+    if data is not None:
+        number_of_items=len(data)
+    return number_of_items * p
+
+def variance_of_binomial_distribution(number_of_items:int,p:Number,data:Optional[list]=None)->Number:
+    """
+    Returns the variance of binomial distribution. \n
+    """
+    if data is not None:
+        number_of_items=len(data)
+    return number_of_items * (1-p)
+
+def standard_deviation_of_binomial_distribution(number_of_items:int,p:Number,data:Optional[list]=None)->Number:
+    """
+    Returns the standard deviation of binomial distribution. \n
+    """
+    if data is not None:
+        number_of_items=len(data)
+    return root(2,(number_of_items * (1-p)))
+
+def Poisson_distribution(m:Number,x:Number,data:Optional[list]=None)->Number:
+    """
+    Returns the Poisson distribution where: \n
+    m = average number of desired outcomes \n
+    x = number of sucesses \n
+    """
+    return ((e**(-m))*(m **x)) / math.factorial(x)
+
+def multinomial_distribution(n:Number,p_of_n:list,p_of_o:list)->Number:
+    """
+    Returns the multinomial distribution of events such that: \n
+    n is the number of events or trials \n
+    p_of_n is an ordered list of the probability that n1, n2, n3 ... occurs. \n
+    p_of_o is an ordered list of the probability of outcome p1, p2, p3 ... \n
+    Formula = \n
+    p = ( n! / (n1!)(n2!)...(nk!)) * ( (p ** n1) (p ** n2) ... (p ** nk) )
+    """
+    top = math.factorial(n)
+    bot = product_notation(None,None,lambda x: math.factorial(x),[p_of_n])
+    end = product_notation(None,None,lambda p, x: p**x,[p_of_o,p_of_n])
+    return (top/bot) * end
+
+def hypergeometric_distribution(Number_in_population:Number,number_in_sample:Number,k_sucess:Number,n_sucess:Number)->Number:
+    """
+    Returns the hypergeometric distribution \n
+    Where: \n
+    Number_in_population = size of population \n
+    number_in_sample = size of the sample \n
+    k_sucess = number of sucesses in the population \n
+    n_sucess = number of sucesses in the sample \n
+    
+    """
+    t = combination(k_sucess,n_sucess) * combination((Number_in_population-k_sucess),(number_in_sample-n_sucess))
+    b = combination(Number_in_population,number_in_sample)
+    return t/b
+
+def mean_of_hypergeometric_distribution(Number_in_population:Number,number_in_sample:Number,k:Number)->Number:
+    """
+    Returns the mean of hypergeometric distribution where: \n
+    N = size of population \n
+    k = sucesses in population \n
+    n = size of sample \n
+    """
+    return (number_in_sample * k) / Number_in_population
+
+def standard_deviation_of_hypergeometric_distribution(Number_in_population:Number,number_in_sample:Number,k:Number)->Number:
+    """
+    Returns the standard deviation of hypergeometric distribution where: \n
+    N = size of population \n
+    k = sucesses in population \n
+    n = size of sample \n
+    """
+    t = number_in_sample * k * (Number_in_population - k) * (Number_in_population - number_in_sample)
+    b = (Number_in_population ** 2) * (Number_in_population - 1)
+    return root(2, (t/b))