Hypothesis Testing – Module 8 – Quantitative Methods – CFA® Level I 2026
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0:00 Introduction: Statistical Inference & Hypothesis Testing
Overview of statistical inference (making judgments from samples)
Why hypothesis testing matters in finance (objective decision-making)
0:53 Key Concepts: Null vs. Alternative Hypothesis
Null hypothesis (default assumption with equality)
Alternative hypothesis (the “challenger”)
How they shape hypothesis testing in finance
1:29 Steps in Hypothesis Testing
State hypotheses (null & alternative)
Choose test statistic (Z, t, etc.)
Set significance level (alpha)
Decision rule & critical values
Collect data & calculate test statistic
Compare to critical region
Make economic/financial decision
3:04 One-Tailed vs. Two-Tailed Tests
Detecting differences in a specific direction (one-tail)
Checking for any significant difference (two-tail)
Examples in investment return hypotheses
5:30 Type I & Type II Errors
Type I (false positive, alpha)
Type II (false negative, beta)
Trade-off between lowering alpha and raising beta
7:06 Testing Means: Single Mean & Differences
Single mean hypothesis test (Z or t)
Two independent means (t-test for independent samples)
Paired samples (before/after testing)
10:56 Testing Variances: Chi-Square & F-Test
Single variance (chi-square distribution)
Comparing two variances (F distribution)
Decision rules for rejecting the null
14:11 Parametric vs. Non-Parametric Tests
Parametric: assumes known distribution (normal), focuses on parameters (mean, variance)
Non-parametric: minimal distributional assumptions (e.g., Mann-Whitney, Wilcoxon)
When to use each method
18:48 Conclusion & CFA Exam Prep Tips
Recap of hypothesis testing importance
Balancing type I and type II errors
Practicing parametric & non-parametric approaches
Encouragement for CFA success