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chelseatroy/distance.py

Last active March 27, 2018 03:48
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T-Tests for Independent Examples
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distance.py
def t_test_for(num_samples_1, standard_deviation_1, mean_1, num_samples_2, standard_deviation_2, mean_2, confidence=0.95):
alpha = 1 - confidence
total_degrees_freedom = num_samples_1 + num_samples_2 - 2
t_distribution_number = -1 * t.ppf(alpha, total_degrees_freedom)
degrees_freedom_1 = num_samples_1 - 1
degrees_freedom_2 = num_samples_2 - 1
sum_of_squares_1 = (standard_deviation_1 ** 2) * degrees_freedom_1
sum_of_squares_2 = (standard_deviation_2 ** 2) * degrees_freedom_2
combined_variance = (sum_of_squares_1 + sum_of_squares_2) / (degrees_freedom_1 + degrees_freedom_2)
first_dividend_addend = combined_variance/float(num_samples_1)
second_dividend_addend = combined_variance/float(num_samples_2)
denominator = math.sqrt(first_dividend_addend + second_dividend_addend)
numerator = mean_1 - mean_2
t_value = float(numerator)/float(denominator)
accept_null_hypothesis = abs(t_value) < abs(t_distribution_number) #results are not significant
return accept_null_hypothesis, t_value
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