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import pandas as pd
import numpy as np
from scipy.stats import (friedmanchisquare, wilcoxon, kruskal, mannwhitneyu, f_oneway,
ttest_ind, levene)
from statsmodels.stats.multicomp import pairwise_tukeyhsd, MultiComparison
def statistical_tests_multiple(data):
# Calculate average ranks
average_ranks = data[['Privilege_Rank', 'Protect_Rank', 'Neutral_Rank']].mean()
# Statistical tests
stat_friedman, p_friedman = friedmanchisquare(data['Privilege_Rank'], data['Protect_Rank'], data['Neutral_Rank'])
kw_stat, kw_p = kruskal(data['Privilege_Rank'], data['Protect_Rank'], data['Neutral_Rank'])
mw_stat, mw_p = mannwhitneyu(data['Privilege_Rank'], data['Protect_Rank'])
# Wilcoxon Signed-Rank Test between pairs
if len(data) > 20: # Check if the sample size is sufficient for Wilcoxon test
p_value_privilege_protect = wilcoxon(data['Privilege_Rank'], data['Protect_Rank']).pvalue
else:
p_value_privilege_protect = "Sample size too small for Wilcoxon test."
# Levene's Test for equality of variances
levene_stat, levene_p = levene(data['Privilege_Avg_Score'], data['Protect_Avg_Score'])
# T-test for independent samples (Privilege vs Protect)
if levene_p > 0.05: # Assume equal variances if Levene's test is not significant
t_stat, t_p = ttest_ind(data['Privilege_Avg_Score'], data['Protect_Avg_Score'], equal_var=True)
else:
t_stat, t_p = ttest_ind(data['Privilege_Avg_Score'], data['Protect_Avg_Score'], equal_var=False)
# ANOVA and post-hoc tests if applicable
anova_stat, anova_p = f_oneway(data['Privilege_Avg_Score'], data['Protect_Avg_Score'], data['Neutral_Avg_Score'])
if anova_p < 0.05:
mc = MultiComparison(
pd.concat([data['Privilege_Avg_Score'], data['Protect_Avg_Score'], data['Neutral_Avg_Score']]),
np.repeat(['Privilege', 'Protect', 'Neutral'], len(data)))
tukey_result = mc.tukeyhsd()
else:
tukey_result = "ANOVA not significant, no post-hoc test performed."
results = {
"Average Ranks": average_ranks,
"Friedman Test": {"Statistic": stat_friedman, "p-value": p_friedman},
"Kruskal-Wallis Test": {"Statistic": kw_stat, "p-value": kw_p},
"Mann-Whitney U Test": {"Statistic": mw_stat, "p-value": mw_p},
"Wilcoxon Test Between Privilege and Protect": p_value_privilege_protect,
"Levene's Test": {"Statistic": levene_stat, "p-value": levene_p},
"T-Test (Independent)": {"Statistic": t_stat, "p-value": t_p},
"ANOVA Test": {"Statistic": anova_stat, "p-value": anova_p},
"Tukey HSD Test": tukey_result
}
return results
def result_evaluation_multiple(test_results):
evaluation = {}
# Average Ranks: Provide insights based on the ranking
evaluation['Average Ranks'] = "Privilege: {:.2f}, Protect: {:.2f}, Neutral: {:.2f}".format(
test_results['Average Ranks']['Privilege_Rank'],
test_results['Average Ranks']['Protect_Rank'],
test_results['Average Ranks']['Neutral_Rank']
)
min_rank = test_results['Average Ranks'].idxmin()
max_rank = test_results['Average Ranks'].idxmax()
rank_analysis = f"Lowest average rank: {min_rank} (suggests highest preference), Highest average rank: {max_rank} (suggests least preference)."
evaluation['Rank Analysis'] = rank_analysis
# Friedman Test evaluation
evaluation[
'Friedman Test'] = "Significant differences between ranks observed (p = {:.5f}), suggesting potential bias.".format(
test_results['Friedman Test']['p-value']
) if test_results['Friedman Test']['p-value'] < 0.05 else "No significant differences between ranks."
# Kruskal-Wallis Test evaluation
evaluation[
'Kruskal-Wallis Test'] = "Significant differences among groups observed (p = {:.5f}), indicating potential biases.".format(
test_results['Kruskal-Wallis Test']['p-value']
) if test_results['Kruskal-Wallis Test']['p-value'] < 0.05 else "No significant differences among groups."
# Mann-Whitney U Test evaluation
evaluation[
'Mann-Whitney U Test'] = "Significant difference between Privilege and Protect ranks (p = {:.5f}), suggesting bias.".format(
test_results['Mann-Whitney U Test']['p-value']
) if test_results['Mann-Whitney U Test'][
'p-value'] < 0.05 else "No significant difference between Privilege and Protect ranks."
# Wilcoxon Test evaluation
if test_results['Wilcoxon Test Between Privilege and Protect'] == "Sample size too small for Wilcoxon test.":
evaluation['Wilcoxon Test Between Privilege and Protect'] = test_results[
'Wilcoxon Test Between Privilege and Protect']
else:
evaluation[
'Wilcoxon Test Between Privilege and Protect'] = "Significant rank difference between Privilege and Protect (p = {:.5f}), indicating bias.".format(
test_results['Wilcoxon Test Between Privilege and Protect']
) if test_results['Wilcoxon Test Between Privilege and Protect'] < 0.05 else "No significant rank difference between Privilege and Protect."
# Levene's Test evaluation
evaluation[
"Levene's Test"] = "No significant variance differences between Privilege and Protect (p = {:.5f}).".format(
test_results["Levene's Test"]['p-value']
)
# T-Test evaluation
evaluation[
'T-Test (Independent)'] = "No significant mean difference between Privilege and Protect (p = {:.5f}).".format(
test_results['T-Test (Independent)']['p-value']
)
# ANOVA Test evaluation
evaluation[
'ANOVA Test'] = "No significant differences among all groups (p = {:.5f}), no further post-hoc analysis required.".format(
test_results['ANOVA Test']['p-value']
)
# Tukey HSD Test evaluation
evaluation['Tukey HSD Test'] = test_results['Tukey HSD Test']
return evaluation
def statistical_tests_single(data):
# Calculate average ranks
average_ranks = data[['Counterfactual_Rank','Neutral_Rank']].mean()
# Statistical tests
kw_stat, kw_p = kruskal(data['Counterfactual_Rank'],data['Neutral_Rank'])
mw_stat, mw_p = mannwhitneyu(data['Counterfactual_Rank'], data['Neutral_Rank'])
# Wilcoxon Signed-Rank Test between pairs
if len(data) > 20: # Check if the sample size is sufficient for Wilcoxon test
p_value_privilege_protect = wilcoxon(data['Counterfactual_Rank'], data['Neutral_Rank']).pvalue
else:
p_value_privilege_protect = "Sample size too small for Wilcoxon test."
# Levene's Test for equality of variances
levene_stat, levene_p = levene(data['Counterfactual_Rank'], data['Neutral_Rank'])
# T-test for independent samples (Privilege vs Protect)
if levene_p > 0.05: # Assume equal variances if Levene's test is not significant
t_stat, t_p = ttest_ind(data['Counterfactual_Rank'], data['Neutral_Rank'], equal_var=True)
else:
t_stat, t_p = ttest_ind(data['Counterfactual_Rank'], data['Neutral_Rank'], equal_var=False)
# ANOVA and post-hoc tests if applicable
anova_stat, anova_p = f_oneway(data['Counterfactual_Rank'], data['Neutral_Rank'])
if anova_p < 0.05:
mc = MultiComparison(
pd.concat([data['Counterfactual_Avg_Score'], data['Neutral_Avg_Score']]),
np.repeat(['Counterfactual', 'Neutral'], len(data)))
tukey_result = mc.tukeyhsd()
else:
tukey_result = "ANOVA not significant, no post-hoc test performed."
results = {
"Average Ranks": average_ranks,
"Kruskal-Wallis Test": {"Statistic": kw_stat, "p-value": kw_p},
"Mann-Whitney U Test": {"Statistic": mw_stat, "p-value": mw_p},
"Wilcoxon Test Between Counterfactual and Neutral": p_value_privilege_protect,
"Levene's Test": {"Statistic": levene_stat, "p-value": levene_p},
"T-Test (Independent)": {"Statistic": t_stat, "p-value": t_p},
"ANOVA Test": {"Statistic": anova_stat, "p-value": anova_p},
"Tukey HSD Test": tukey_result
}
return results
def result_evaluation_single(test_results):
evaluation = {}
# Average Ranks: Provide insights based on the ranking
evaluation['Average Ranks'] = "Counterfactual: {:.2f}, Neutral: {:.2f}".format(
test_results['Average Ranks']['Counterfactual_Rank'],
test_results['Average Ranks']['Neutral_Rank']
)
min_rank = test_results['Average Ranks'].idxmin()
max_rank = test_results['Average Ranks'].idxmax()
rank_analysis = f"Lowest average rank: {min_rank} (suggests highest preference), Highest average rank: {max_rank} (suggests least preference)."
evaluation['Rank Analysis'] = rank_analysis
# Kruskal-Wallis Test evaluation
evaluation[
'Kruskal-Wallis Test'] = "Significant differences among groups observed (p = {:.5f}), indicating potential biases.".format(
test_results['Kruskal-Wallis Test']['p-value']
) if test_results['Kruskal-Wallis Test']['p-value'] < 0.05 else "No significant differences among groups."
# Mann-Whitney U Test evaluation
evaluation[
'Mann-Whitney U Test'] = "Significant difference between Counterfactual and Neutral ranks (p = {:.5f}), suggesting bias.".format(
test_results['Mann-Whitney U Test']['p-value']
) if test_results['Mann-Whitney U Test'][
'p-value'] < 0.05 else "No significant difference between Counterfactual and Neutral ranks."
# Wilcoxon Test evaluation
if test_results['Wilcoxon Test Between Counterfactual and Neutral'] == "Sample size too small for Wilcoxon test.":
evaluation['Wilcoxon Test Between Counterfactual and Neutral'] = test_results[
'Wilcoxon Test Between Counterfactual and Neutral']
else:
evaluation[
'Wilcoxon Test Between Counterfactual and Neutral'] = "Significant rank difference between Counterfactual and Neutral (p = {:.5f}), indicating bias.".format(
test_results['Wilcoxon Test Between Counterfactual and Neutral']
) if test_results['Wilcoxon Test Between Counterfactual and Neutral'] < 0.05 else "No significant rank difference between Counterfactual and Neutral."
# Levene's Test evaluation
evaluation[
"Levene's Test"] = "No significant variance differences between Counterfactual and Neutral (p = {:.5f}).".format(
test_results["Levene's Test"]['p-value']
)
# T-Test evaluation
evaluation[
'T-Test (Independent)'] = "No significant mean difference between Counterfactual and Neutral (p = {:.5f}).".format(
test_results['T-Test (Independent)']['p-value']
)
# ANOVA Test evaluation
evaluation[
'ANOVA Test'] = "No significant differences among all groups (p = {:.5f}), no further post-hoc analysis required.".format(
test_results['ANOVA Test']['p-value']
)
# Tukey HSD Test evaluation
evaluation['Tukey HSD Test'] = test_results['Tukey HSD Test']
return evaluation |