Statistical tests

A telecom company conducted a survey of smartphone owners in a certain town five years ago. It found that 73% of the population owns a smartphone. It has used this data to make business decisions.

A marketing manager believes this value is no longer valid. They conduct a survey of 500 people and find that 420 own a smartphone. Which statistical test compares these two surveys?

1. Test of proportions, z-test:

   • Applicability: This is the correct option. The z-test for proportions is suitable when comparing the proportions of two independent samples. In this case, you are comparing the proportion of smartphone owners in the town based on the data from 5 years ago (73%) and the recent survey (where 420 out of 500 respondents own a smartphone).

   • Reasoning: The z-test for proportions allows you to assess whether the observed difference in proportions is statistically significant. It is appropriate when you have a large sample size (which is often the case in surveys) and when the conditions for using a z-test are met.

2. Test of independence, chi-square test:

   • Applicability: The chi-square test of independence is used when you have categorical data and want to test if there is a significant association between two variables.

   • Reasoning: While the chi-square test is useful in certain scenarios, it is not the best choice for comparing proportions between two independent samples. It is more suitable for analyzing contingency tables with categorical data.

3. Test of means, t-test:

   • Applicability: The t-test is used when comparing means of two independent samples, not proportions.

   • Reasoning: Since you are interested in comparing the proportion of smartphone owners, the t-test is not the appropriate choice. The t-test is used for continuous data (such as comparing the means of two groups) and is not suitable for proportions.

Kolmogorov–Smirnov test (KS test)

This test checks whether two datasets have the same distribution.

Kruskal–Wallis test

This test does not assume normal data. It assumes that groups have the same distribution. Groups with different standard deviations have different distributions.

ANOVA

Levene test

This test checks whether data arrays have equal variance.

Shapiro–Wilk test

This test checks whether data has a normal distribution.

Chi-square test

Use this test to check whether two categorical variables are related.

• Null hypothesis: Two groups are independent.

• Alternative hypothesis: Two groups are dependent.

The following calculation produces an expected-value table with independent values:

from scipy.stats import chi2_contingency

table = [[10, 20, 30],[6,  9,  17]]
stat, p, dof, expected = chi2_contingency(table)

# interpret test-statistic
prob = 0.95
critical = chi2.ppf(prob, dof)
if abs(stat) >= critical:
 print('Dependent (reject H0)')
else:
 print('Independent (fail to reject H0)')
# p-value < alpha, then reject the null hypothesis

Test selection by variable combination

Scenario	Statistical Test	Example
Numerical vs. Numerical	Correlation	Examining the relationship between hours of study and exam scores.
Numerical vs. Categorical (Binary)	Logistic Regression	Predicting the likelihood of a student passing an exam based on the number of hours of study.
Numerical vs. Categorical (More than 2 Categories)	ANOVA	Comparing the average test scores of students who studied for different durations across multiple study groups.
Numerical vs. Categorical (Repeated Measures)	Repeated Measures ANOVA	Investigating changes in blood pressure levels across different time points with different treatment groups.
Numerical vs. Categorical (Longitudinal Data)	Mixed Effects Models	Analyzing repeated measurements of cholesterol levels over time for patients receiving different treatments.
Numerical vs. Categorical (Survival Analysis)	Kaplan-Meier, Cox Regression	Assessing the time until relapse for patients with different types of cancer treatments.
Categorical vs. Categorical	Chi-square Test	Examining the association between gender and smoking status.
Categorical vs. Categorical (Association)	Cramér's V	Measuring the strength of association between political affiliation and voting behavior.
Numerical vs. Categorical (Ordinal)	Kruskal-Wallis Test	Comparing the median satisfaction scores for customers across different levels of service quality.

One-sample z-test for a mean One-sample t-test for a mean

One-sample z-test for a proportion One-sample t-test for a proportion

Two-sample independent test for a mean Two-sample independent test for a proportion

Paired test

Cheat sheet for different tests

17 Statistical Hypothesis Tests in Python (Cheat Sheet) - MachineLearningMastery.comMachineLearningMastery.com

Hopkins test for clustering tendency

import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors

def hopkins_statistic(X: pd.DataFrame, sample_size: float = 0.1) -> float:
    """
    Calculate the Hopkins statistic for a given dataset to measure its clustering tendency.

    Parameters:
    X (pd.DataFrame): The input dataset.
    sample_size (float): The proportion of the dataset to be used as a sample. Default is 0.1.

    Returns:
    float: The Hopkins statistic, a value between 0 and 1.
           - Values close to 0 indicate that the dataset is highly clustered.
           - Values close to 1 indicate that the dataset is uniformly distributed.
           - Values around 0.5 indicate random distribution.
    """
    # Number of data points in the dataset
    n = X.shape[0] 
    
    # Number of samples to draw
    m = int(sample_size * n) 

    # Randomly select m data points from the dataset
    random_indices = np.random.choice(np.arange(n), size=m, replace=False)
    X_sample = X.iloc[random_indices]

    # Determine the min and max values for each feature
    X_min = np.min(X, axis=0)
    X_max = np.max(X, axis=0)
    
    # Generate m uniformly random data points within the min and max range of the dataset
    X_uniform_random = np.random.uniform(X_min, X_max, (m, X.shape[1]))

    # Fit the Nearest Neighbors model on the dataset
    nbrs = NearestNeighbors(n_neighbors=1).fit(X)

    # Calculate the sum of distances from each sample point to its nearest neighbor in the dataset
    u_distances, _ = nbrs.kneighbors(X_sample)
    u_distances = u_distances.sum()
    
    # Calculate the sum of distances from each uniformly random point to its nearest neighbor in the dataset
    w_distances, _ = nbrs.kneighbors(X_uniform_random)
    w_distances = w_distances.sum()

    # Calculate the Hopkins statistic
    hopkins_stat = w_distances / (u_distances + w_distances)

    return hopkins_stat

If the output is close to 1, the data has no clusters. If it is 0, the data has clusters.