OMBA 102 · Unit 4 of 7
Statistical Inference
Data, Statistics and Managerial Decisions
Start unit · 5 lessons →Learning objectives
After completing this unit, you will be able to:
- Distinguish samples from populations and quantify sampling error
- Build and interpret confidence intervals for business metrics
- Conduct hypothesis tests with correct managerial framing
- Separate statistical significance from business significance
- Avoid common errors in statistical reasoning (p-hacking, multiple comparisons, etc.)
Why this matters
A/B tests, surveys, and pilots drive billion-dollar decisions. Unit 4 teaches you what inferential statistics can and cannot prove so you do not overreact to noise or underreact to real signal.
Unit overview
Work through the five lessons below in order. Work examples with real or provided sample sizes.
| # | Lesson | Core idea |
|---|---|---|
| 1 | Samples, Populations, and Sampling Error | Representative data and margin of error |
| 2 | Confidence Intervals | Range estimates for means and proportions |
| 3 | Hypothesis Testing | Null, alternative, and test logic |
| 4 | Statistical Significance versus Business Significance | Practical importance vs p-values |
| 5 | Common Errors in Statistical Reasoning | Pitfalls managers must catch |
Connection to applied work
Design a simple test plan for one project hypothesis: population, sample, metric, and what result would change your decision.
Practice
- Explain why a survey of only power users biases inference.
- Compute a confidence interval for a proportion (use provided data).
- Frame a hypothesis test for a pricing experiment in plain English.
- Name one result that is statistically significant but not worth acting on.
Knowledge check
- What is sampling error?
- How do you interpret a 95% confidence interval?
- What does a p-value not tell you?
- What is business significance?
- What is p-hacking?
Key takeaways
- Inference generalizes from samples with stated uncertainty.
- Significance is not the same as importance.
- Skepticism is part of data literacy.
- Finish lessons before assessments.
Unit assessment
Complete each section below. Score 80%+ on the quiz to finish this unit's assessment.
Exercises
Apply what you learned in this unit with structured practice.
Deliverable
300–500 word analysis document saved to your portfolio under OMBA 102.
Rubric
- • Framework applied correctly (not just named)
- • Specific evidence from a real example
- • Clear recommendation with tradeoffs acknowledged
- • Professional writing with source citation
Deliverable
Problem solutions + 150-word reflection in your OMBA 102 workbook.
Rubric
- • Attempted all practice items before checking answers
- • Honest reflection on errors
- • Identifies a specific review action
Model / spreadsheet
Build or extend a spreadsheet model tied to this unit.
Deliverable
Structured model document (2+ pages) · One-paragraph summary of key insight from the model · Screenshot or export saved to portfolio
Rubric
- • Assumptions stated explicitly
- • Logic is auditable (formulas or steps visible)
- • Output answers a specific business question
- • Sensitivity or scenario considered
Knowledge quiz
Check your understanding before marking the unit complete.
1. A sample of 400 visitors yields 42 conversions. The population parameter of interest is:
2. A 95% confidence interval for conversion is 3.7% to 4.3%. Which executive read is most appropriate?
3. In a two-sided A/B test of conversion, the null hypothesis H₀ is typically:
4. A p-value of 0.03 in a well-designed test means:
5. Rejecting a true null hypothesis at α = 0.05 is called:
6. A test shows statistically significant 0.1% conversion lift on 2 million users, worth $8,000 annually, while implementation costs $500,000. Best conclusion?
7. Running 20 independent tests at α = 0.05 without adjustment makes false positives:
8. Convenience sampling website visitors on Tuesday morning primarily threatens: