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Descriptive Statistics Memo
Calculate and interpret mean, median, mode, and standard deviation for a salary dataset.
Hypothesis Testing (A/B Test)
Determine if a website change actually improved conversion, or if it was just noise.
Correlation vs. Causation Case Study
Tear apart a misleading business report that confuses correlation with causation.
Statistical Analysis & A/B Testing Hypothesis Testing
Data-driven decisions require rigorous statistical validation. Employers look for candidates who design A/B test experiments, conduct hypothesis tests (t-test, chi-square, ANOVA), interpret p-values correctly, and calculate confidence intervals.
1. A/B Test Experimentation Specification
Experiment plan specifying null/alternative hypotheses, sample size calculations, and primary metrics.
2. Python Statistical Analysis Script
Python script using scipy.stats to run two-sample t-tests, chi-square tests, and confidence interval calculations.
3. Statistical Findings & Decision Report
Report evaluating statistical significance (p-value), effect size, and practical business recommendations.
Frequently Asked Questions (Statistical Thinking)
What does a p-value less than 0.05 actually mean?
A p-value < 0.05 indicates there is less than a 5% probability that the observed result occurred by random chance under the null hypothesis.
What is A/B test peeking bias?
Peeking bias occurs when analysts check experiment results continuously and stop the test early as soon as p < 0.05, artificially inflating false positive rates.
How do you calculate required sample sizes for an A/B test?
Sample sizes are calculated using statistical power (typically 80%), significance level (alpha = 0.05), baseline conversion rate, and Minimum Detectable Effect (MDE).
Explore Data Analytics Career Paths
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