Usage Guide
Installation
The package can be installed using pip. The dependdencies are pandas,numpy, scipy, lifelines, and statsmodels.
Quick Start
Detailed user example
# Generate a sample data to test the package
import numpy as np
import pandas as pd
np.random.seed(123)
n = 100
# 1. Independent groups for t-test / one-way ANOVA / two-way ANOVA (factor A/B)
group_bin = np.random.choice([0, 1], size=n)
factorA = np.random.choice(['A1','A2'], size=n)
factorB = np.random.choice(['B1','B2'], size=n)
# 2. Continuous variables (for t-tests, ANOVAs, correlations, regressions, etc.)
cont_var1 = np.random.normal(loc=50, scale=10, size=n)
cont_var2 = np.random.normal(loc=0, scale=5, size=n)
cont_var3 = np.random.normal(loc=100, scale=20, size=n)
# 3. Repeated-measures variables (for repeated-measures ANOVA)
rm_time1 = np.random.normal(loc=5, scale=1, size=n)
rm_time2 = rm_time1 + np.random.normal(loc=0.5, scale=0.5, size=n)
rm_time3 = rm_time1 + np.random.normal(loc=1.0, scale=0.5, size=n)
# 4. Paired data (for paired t-tests or Wilcoxon signed-rank)
paired_pre = np.random.normal(loc=10, scale=2, size=n)
paired_post = paired_pre + np.random.normal(loc=-1, scale=1, size=n)
# 5. Logistic outcome (binary) for logistic regression
logistic_outcome = np.random.binomial(n=1, p=0.4, size=n)
# 6. Categorical variables (for Chi-Square)
cat_var1 = np.random.choice(['Yes','No'], size=n)
cat_var2 = np.random.choice(['High','Low'], size=n)
# 7. Survival data (time-to-event + event indicator for KM/Cox)
time_to_event = np.random.exponential(scale=10, size=n)
event_occurred = np.random.binomial(n=1, p=0.7, size=n)
# 8. Count data (for Poisson regression)
count_data = np.random.poisson(lam=2, size=n)
# 9. Ordinal data (for Spearman correlation or ordinal logistic)
ordinal_data = np.random.choice(['Mild','Moderate','Severe'], size=n)
# 10. Additional continuous variables for correlations / MANOVA
manova_var1 = np.random.normal(loc=30, scale=5, size=n)
manova_var2 = np.random.normal(loc=60, scale=10, size=n)
# Assemble everything into a DataFrame
data = pd.DataFrame({
'group_bin': group_bin,
'factorA': factorA,
'factorB': factorB,
'cont_var1': cont_var1,
'cont_var2': cont_var2,
'cont_var3': cont_var3,
'rm_time1': rm_time1,
'rm_time2': rm_time2,
'rm_time3': rm_time3,
'paired_pre': paired_pre,
'paired_post': paired_post,
'logistic_outcome': logistic_outcome,
'cat_var1': cat_var1,
'cat_var2': cat_var2,
'time_to_event': time_to_event,
'event_occurred': event_occurred,
'count_data': count_data,
'ordinal_data': ordinal_data,
'manova_var1': manova_var1,
'manova_var2': manova_var2
})
print(data.head(5))
group_bin factorA factorB cont_var1 cont_var2 cont_var3 rm_time1 \
0 0 A2 B2 54.743473 -6.186766 96.147700 4.413384
1 1 A1 B2 44.360761 0.620279 108.982712 5.154290
2 0 A1 B1 40.026785 -8.002203 97.092729 3.852763
3 0 A2 B1 38.999569 3.769344 137.374529 6.520166
4 0 A2 B1 42.435628 -1.234079 89.625923 5.189043
rm_time2 rm_time3 paired_pre paired_post logistic_outcome cat_var1 \
0 5.256786 5.338739 10.545469 8.320940 0 No
1 5.947174 7.147672 10.850672 10.859323 0 Yes
2 4.585648 4.515028 9.538192 8.055827 1 No
3 6.311219 7.372252 17.143158 17.222955 0 Yes
4 5.909335 5.162844 9.207688 7.786610 1 No
cat_var2 time_to_event event_occurred count_data ordinal_data \
0 Low 0.491169 1 5 Severe
1 Low 21.322712 1 2 Severe
2 Low 6.101167 1 0 Severe
3 Low 17.788497 1 1 Moderate
4 High 3.188856 0 1 Mild
manova_var1 manova_var2
0 35.227235 49.717239
1 25.034866 52.544302
2 32.711234 56.899888
3 32.427786 48.581779
4 33.709235 69.263657
T-tests
# for independent t-test
# ---------------------
# Initialize checker
checker = ash.StatisticalTestAssumptions()
# Check assumptions for independent t-test
results = checker.check_assumptions(
data=data,
test_type='t_test_ind',
variables=['cont_var1', 'cont_var2'],
group_column='group_bin'
)
# Get recommendation
recommendation = checker.get_recommendation(results)
print(recommendation)
✓ All assumptions are met. You can proceed with the Independent t-test.
# for paired t-test
# ---------------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='paired_ttest',
variables=['paired_pre', 'paired_post']
)
# Get recommendations
recommendation = checker.get_recommendation(results)
print(recommendation)
✓ All assumptions are met. You can proceed with the Paired t-test.
ANOVAs
# One-way ANOVA
# -------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='one_way_anova',
variables=['cont_var1', 'cont_var2'],
group_column='group_bin'
)
recommendation = checker.get_recommendation(results)
print(recommendation)
✓ All assumptions are met. You can proceed with the One-way ANOVA.
# Two-Way ANOVA
# ----------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='two_way_anova',
variables=['cont_var1'],
#dependent_var='cont_var1',
factors=['factorA', 'factorA']
)
recommendation = checker.get_recommendation(results)
print(recommendation)
✓ All assumptions are met. You can proceed with the Two-way ANOVA.
# Factorial (Two-way) ANOVA
# -----------------------
checker = ash.StatisticalTestAssumptions()
# Sample data structure
data2 = pd.DataFrame({
'fertilizer': ['A', 'A', 'B', 'B'] * 25,
'watering': ['daily', 'weekly'] * 50,
'yield': np.random.normal(loc=[50, 45, 60, 55] * 25, scale=5)
})
results = checker.check_assumptions(
data=data2,
test_type='factorial_anova',
variables=['yield'],
group_columns= ['fertilizer', 'watering']
)
recommendation = checker.get_recommendation(results)
print(recommendation)
⚠ Some assumptions for Factorial ANOVA are violated:
- Insufficient sample size in some cells (minimum 25 < required 30)
Consider these alternatives:
- Non-parametric factorial analysis
- Mixed-effects model
- Robust ANOVA
# Repeated measures ANOVA
#-------------------------
checker = ash.StatisticalTestAssumptions()
# Check assumptions
results = checker.check_assumptions(
data=data,
test_type='repeated_anova',
variables=['rm_time1', 'rm_time2', 'rm_time3'],
subject_column='group_bin'
)
recommendation = checker.get_recommendation(results)
print(recommendation)
✓ All assumptions are met. You can proceed with the Repeated Measures ANOVA.
# for MANOVA (Multivariate Analysis of Variance)
#---------------------------------------------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='manova',
variables=['manova_var1', 'manova_var2'],
group_col='group_bin'
)
recommendation = checker.get_recommendation(results)
print(recommendation)
⚠ Some assumptions for MANOVA are violated:
- Multivariate normality violated in group_1
- Number of dependent variables should ideally be greater than number of groups
Consider these alternatives:
- Separate univariate ANOVAs with Bonferroni correction
- Robust MANOVA
- Permutation MANOVA
- Non-parametric multivariate tests (e.g., NPMANOVA)
- Linear Discriminant Analysis
Correlation tests
# for Pearson corraltion
# --------------------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='pearson_correlation',
variables=['cont_var1', 'cont_var2']
)
recommendation = checker.get_recommendation(results)
print(recommendation)
⚠ Some assumptions for Pearson Correlation are violated:
- Variable pair cont_var1_vs_cont_var2 may not have a monotonic relationship (Spearman correlation=0.07)
Consider these alternatives:
- Spearman rank correlation
- Kendall rank correlation
- Robust correlation methods
# for spearman correlation
#----------------------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='spearman',
variables=['ordinal_data', 'cont_var2']
)
recommendation = checker.get_recommendation(results)
print(recommendation)
✓ All assumptions are met. You can proceed with the Spearman's Rank Correlation.
Chi-square independence
# for Chi-square test
# ---------------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='chi_square_independence',
variables=['cat_var1', 'cat_var1']
)
recommendation = checker.get_recommendation(results)
print(recommendation)
✓ All assumptions are met. You can proceed with the Chi-square test of independence.
Regression
# For Logistic Regression
#------------------------
checker = ash.StatisticalTestAssumptions()
# Check assumptions
results = checker.check_assumptions(
data=data,
test_type='logistic',
variables=['cont_var1', 'cont_var2', 'cont_var3'],
dependent_var='logistic_outcome'
)
recommendation = checker.get_recommendation(results)
print(recommendation)
⚠ Some assumptions for Logistic Regression are violated:
- High multicollinearity detected for 'const' (VIF=58.70)
Consider these alternatives:
- Penalized regression (Ridge, Lasso)
- Decision trees
# Multiple Linear Regression
# ----------------------------
checker = StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='multiple_regression',
variables=['cont_var1', 'cont_var2', 'cont_var3'],
dependent_var='logistic_outcome'
)
recommendation = checker.get_recommendation(results)
print(recommendation)
⚠ Some assumptions for Multiple Linear Regression are violated:
- Residuals are not normally distributed (Shapiro-Wilk p=0.0000)
- Non-linear relationship detected for predictor 'cont_var1'
- Non-linear relationship detected for predictor 'cont_var2'
- High multicollinearity detected for variables: ['const']
# for Poisson regression
#-----------------------------------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='poisson',
variables=[
'count_data', # dependent variable must be first
'cont_var1', # predictors follow
'cont_var2'
],
offset_var='exposure_time' # Optional
)
recommendation = checker.get_recommendation(results)
print(recommendation)
⚠ Some assumptions for Poisson Regression are violated:
- High multicollinearity detected for variables: ['const']
Consider these alternatives:
- Negative Binomial Regression
- Zero-inflated Poisson Regression
- Zero-inflated Negative Binomial Regression
- Quasi-Poisson Regression
Survival analysis
# for Kaplan-Meier
# ----------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='kaplan_meier',
variables=['time_to_event', 'event_occurred'], # time variable first, event variable second
group_col='group_bin' # optional grouping variable
)
recommendation = checker.get_recommendation(results)
print(recommendation)
✓ All assumptions are met. You can proceed with the Kaplan-Meier survival analysis.
# for Cox Proportional Hazards
# -----------------------------
checker = ash.StatisticalTestAssumptions()
cox_results = checker.check_assumptions(
data=data,
test_type='cox_ph',
variables=['time_to_event', 'event_occurred'], # time variable first, event variable second
group_col='group_bin'
)
recommendation = checker.get_recommendation(cox_results)
print(recommendation)
✓ All assumptions are met. You can proceed with the Cox Proportional Hazards Regression.
Non-parametric tests
# for Wilcoxon Signed-Rank Test
#-------------------------------
checker = ash.StatisticalTestAssumptions()
results = checker.check_assumptions(
data=data,
test_type='wilcoxon_signed_rank',
variables=['paired_pre', 'paired_post']
)
recommendation = checker.get_recommendation(results)
print(recommendation)
✓ All assumptions are met. You can proceed with the Wilcoxon Signed-Rank Test.
Common issues and solutions
1. Handling missing data
AssumptionSheriff automatically handles missing data in most cases. However, for best results:
- Remove or impute missing values before checking assumptions
- Ensure complete cases for paired tests
- Document any data preprocessing steps
2. Dealing with outliers
When outliers are detected: - Review them for data entry errors - Consider robust statistical methods - Document justification for outlier handling
3. Small sample sizes
For small samples: - Consider non-parametric alternatives - Use exact tests when available - Be cautious with assumption violations