Hypothesis Testing

Statistical inference tools for common tests and regression models.

Purpose

Use this module to run hypothesis tests, compare groups, and fit simple models.

Key functions

• ChiSquareTestOfIndependence — Chi-square test from two categorical columns

• ChiSquareTestOfIndependenceFromTable — Chi-square test from a contingency table

• OneSampleTTest — One-sample t-test against a hypothesized mean

• TwoSampleTTestOfIndependence — Independent samples t-test between groups

• TwoSampleTTestPaired — Paired samples t-test for before/after designs

• OneWayANOVA — One-way ANOVA across multiple groups

• TTestOfMeanFromStats — One-sample t-test from summary statistics

• TTestOfTwoMeansFromStats — Two-sample mean comparison from summary statistics

• TTestOfProportionFromStats — One-sample proportion test from summary statistics

• ConductLinearRegressionAnalysis — Linear regression with optional diagnostics

• ConductLogisticRegressionAnalysis — Logistic regression for binary outcomes

• ConductCoxProportionalHazardRegression — Cox proportional hazards survival model

Common use cases

• Categorical association testing (contingency tables)

• Mean comparisons (one-sample, independent, paired)

• Multi-group mean comparisons (ANOVA)

• Working from summary stats instead of raw data

• Regression modeling (linear, logistic)

• Survival analysis (time-to-event)

Examples

See Hypothesis Testing Examples for clean, copy-paste snippets.

Legacy draft (collapsed)

Examples assume you already have a pandas.DataFrame (named df) in memory.

ChiSquareTestOfIndependence

TwoSampleTTestPaired

Performs a chi-square test of independence between two categorical variables.

import pandas as pd
from analysistoolbox.hypothesis_testing import ChiSquareTestOfIndependence

data = {
    'Education': ['High School', 'College', 'High School', 'Graduate', 'College'],
    'Employment': ['Employed', 'Unemployed', 'Employed', 'Employed', 'Unemployed']
}
df = pd.DataFrame(data)

ChiSquareTestOfIndependence(
    dataframe=df,
    first_categorical_column='Education',
    second_categorical_column='Employment',
    plot_contingency_table=True
)

ChiSquareTestOfIndependenceFromTable

Performs a chi-square test from a precomputed contingency table.

import pandas as pd
from analysistoolbox.hypothesis_testing import ChiSquareTestOfIndependenceFromTable

contingency_table = pd.DataFrame(
    {'Online': [100, 150], 'In-Store': [200, 175]},
    index=['Male', 'Female']
)

ChiSquareTestOfIndependenceFromTable(
    contingency_table=contingency_table,
    plot_contingency_table=True
)

OneSampleTTest

Performs a one-sample t-test against a hypothesized mean.

from analysistoolbox.hypothesis_testing import OneSampleTTest

OneSampleTTest(
    dataframe=df,
    outcome_column='score',
    hypothesized_mean=70,
    alternative_hypothesis='two-sided',
    confidence_interval=0.95
)

TwoSampleTTestOfIndependence

Performs an independent samples t-test to compare means between two groups.

Runs a chi-square test using two categorical columns.

Performs a paired samples t-test for before/after comparisons.

from analysistoolbox.hypothesis_testing import TwoSampleTTestOfIndependence

TwoSampleTTestOfIndependence(
    dataframe=df,
    outcome_column='score',
    grouping_column='group',
    alternative_hypothesis='two-sided',
    homogeneity_of_variance=True
)

TwoSampleTTestPaired

Performs a one-way ANOVA to compare means across multiple groups.

from analysistoolbox.hypothesis_testing import TwoSampleTTestPaired

TwoSampleTTestPaired(
    dataframe=df,
    first_outcome_column='pre_score',
    second_outcome_column='post_score',
    alternative_hypothesis='greater'
)

TTestOfMeanFromStats

OneWayANOVA

from analysistoolbox.hypothesis_testing import OneWayANOVA

OneWayANOVA(
    dataframe=df,
    outcome_column='performance',
    grouping_column='treatment_group',
    plot_sample_distributions=True
)

Compares two means using summary statistics.

TTestOfTwoMeansFromStats

Performs a one-sample t-test using summary statistics rather than raw data.

Tests a sample proportion against a hypothesized value.

TTestOfProportionFromStats

from analysistoolbox.hypothesis_testing import TTestOfMeanFromStats

TTestOfMeanFromStats(
    sample_mean=75,
    sample_size=30,
    sample_standard_deviation=10,
    hypothesized_mean=70,
    alternative_hypothesis='greater'
)

Performs linear regression analysis with optional diagnostics.

ConductLinearRegressionAnalysis

from analysistoolbox.hypothesis_testing import TTestOfTwoMeansFromStats

TTestOfTwoMeansFromStats(
    first_sample_mean=75,
    first_sample_size=30,
    first_sample_standard_deviation=10,
    second_sample_mean=70,
    second_sample_size=30,
    second_sample_standard_deviation=12
)

Performs logistic regression for binary outcomes.

ConductLogisticRegressionAnalysis

from analysistoolbox.hypothesis_testing import TTestOfProportionFromStats

TTestOfProportionFromStats(
    sample_proportion=0.65,
    sample_size=200,
    hypothesized_proportion=0.50,
    alternative_hypothesis='two-sided'
)

Performs survival analysis using Cox proportional hazards regression.

ConductCoxProportionalHazardRegression

from analysistoolbox.hypothesis_testing import ConductLinearRegressionAnalysis

results = ConductLinearRegressionAnalysis(
    dataframe=df,
    outcome_column='sales',
    list_of_predictor_columns=['advertising', 'price'],
    plot_regression_diagnostic=True
)

from analysistoolbox.hypothesis_testing import ConductLogisticRegressionAnalysis

results = ConductLogisticRegressionAnalysis(
    dataframe=df,
    outcome_column='purchased',
    list_of_predictor_columns=['age', 'income'],
    plot_regression_diagnostic=True
)

from analysistoolbox.hypothesis_testing import ConductCoxProportionalHazardRegression

model = ConductCoxProportionalHazardRegression(
    dataframe=df,
    outcome_column='event',
    duration_column='time',
    list_of_predictor_columns=['age', 'sex', 'treatment'],
    plot_survival_curve=True
)