Estimation and Inference – Module 7 – Quantitative Methods – CFA® Level I 2026
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0:00 Introduction to Estimation & Inference (CFA Level 1)
Why sampling matters in finance
Using sample data to infer population characteristics
0:22 Basic Terms & Why We Sample
Population vs. sample
Parameters (population) vs. statistics (sample)
Saving time, cost, and effort
1:00 Probability Sampling vs. Nonprobability Sampling
Probability sampling (equal chance for all)
Nonprobability sampling (judgment-based or convenience)
Advantages and drawbacks of each
1:59 Simple Random Sampling & Systematic Sampling
Simple random sampling (lottery approach)
Systematic sampling (select every k-th member)
Sampling error: sample mean vs. population mean
3:13 Stratified & Cluster Sampling
Stratified random sampling: dividing by subgroups (strata)
Cluster sampling: selecting entire clusters (one-stage or two-stage)
Efficiency and representativeness considerations
4:44 Nonprobability Methods: Convenience & Judgmental Sampling
Quick, less resource-intensive but may be biased
Examples in exploratory vs. rigorous studies
5:57 Central Limit Theorem (CLT)
Sampling distribution of the mean approaches normality (n ≥ 30)
Key for confidence intervals and hypothesis testing
7:20 Standard Deviation vs. Standard Error
Measuring spread of data vs. accuracy of sample mean
Practical example (student test scores)
8:13 Resampling: Bootstrapping & Jackknife
Resampling from observed data to estimate statistics
Bootstrapping (with replacement) for standard error & CIs
Jackknife (leave-one-out) method for bias reduction
10:26 Bootstrapping Example & Jackknife Illustration
Calculating mean & standard error using bootstrapping
Jackknife for small data sets (e.g., 5 years of returns)
Influence of each observation on overall mean
11:42 Conclusion & CFA Exam Tips
Summarizing key sampling and inference methods
Importance of hands-on practice (CFA curriculum examples)
Encouragement for further questions and clarifications