Week 13 — Readings & Resources

Your primary reading is Chapter 13 (in this module). Everything below is the optional, go-deeper layer — pick what helps, skip the rest.

How to use this page

This page has 10 items (4 readings and 6 videos). Allow 84–95 minutes for everything. Each group points back to the matching chapter section and class-session segment, so use the full menu when you want more practice, or take the quick path below.

Every video here has captions: turn them on with the CC button in the player. The chapter covers the same ideas in text.

Pick-one quick path (16–20 min total)

• An Introduction to Hypothesis Testing — jbstatistics
• What Is The Null Hypothesis & When To Reject It — Simply Psychology

① Is the student guessing? Hypotheses and the logic of a test

Chapter 13, Section “The logic of a test: assume the null, then measure surprise” · Session 1, Segment 1, “Hypotheses and the logic of a test” · Remember: assume the boring explanation is true, then ask how surprising your data would be if it were.

• An Introduction to Hypothesis Testing — jbstatistics — a careful first look at a test. It uses a binomial model where a pure guess is right one time in four, much like the chapter's guessing quiz. Watch how the claim is assumed true and the data are then judged against it. (video, 10 min) (captions: creator)
• How P-Values Help Us Test Hypotheses: Crash Course Statistics #21 — CrashCourse — a lively tour of significance testing: assume there is no difference, then ask whether the data are rare enough to reject that. The episode talks about a p-value cutoff; this course calls that cutoff the significance level. This builds on Week 11's work with confidence intervals and the margin of error. (video, 12 min) (captions: creator)
• What Is The Null Hypothesis & When To Reject It — Simply Psychology — read the key takeaways, then the parts from How to Write a Null Hypothesis through Why Do We Never Accept the Null Hypothesis: the null and the alternative, deciding with a p-value, the two kinds of error, and why we say fail to reject instead of accept. The last FAQ shows an alternative that points one way or both ways. Where the page says a rejection shows an effect exists, read it as evidence against the null, as the chapter does. Keep it handy while completing the Week-13 module overview. (reading, 6–10 min)

② What a p-value tells you, and what it never tells you

Chapter 13, Section “What a p-value is not: three wrong readings” · Session 1, Segment 2, “p-values and what they do not mean” · Remember: a p-value says how surprising the data would be if the null hypothesis were true. It is not the chance the null is true, and it is not the size of an effect.

• p-values: What they are and how to interpret them — StatQuest with Josh Starmer — a friendly, picture-first look at what a p-value measures. Near the end it makes a key point: a small p-value does not mean a big effect. (video, 11 min) (captions: automatic)
• P-Value Problems: Crash Course Statistics #22 — CrashCourse — the follow-up episode on how p-values get misread and why a fixed cutoff can mislead. Compare each misreading it names with the three wrong readings in the chapter. (video, 12 min) (captions: creator)
• How to Correctly Interpret P Values — Minitab Blog — a short post that sets a correct reading of a p-value next to a common wrong one, using a vaccine study. Read through that side-by-side comparison, then stop at the heading on the true error rate, which goes beyond this course. (reading, 5–8 min)

③ Two ways to be wrong: Type I errors, Type II errors, and power

Chapter 13, Section “Type I and Type II errors: two ways to be wrong” · Session 2, Segment 3, “Decisions, errors, and power” · Remember: a Type I error rejects a true null hypothesis, a Type II error misses a false one, and a stricter cutoff trades fewer of the first for more of the second.

• Type I Errors, Type II Errors, and the Power of the Test — jbstatistics — a calm, exact walk through the two ways a test can be wrong, the chance of each, and the power of a test. (video, 8 min) (captions: creator)
• Errors and Power in Hypothesis Testing: Statistics Tutorial #16 — MarinStatsLectures-R Programming & Statistics — several worked examples, including a disease-testing one, that sort each decision into a two-by-two table of right and wrong calls. It also calls power sensitivity, the medical name for the same idea. (video, 13 min) (captions: automatic)
• 7.1.3. What are statistical tests? — NIST/SEMATECH e-Handbook of Statistical Methods — a free government handbook page set in a factory. Read from the top through the part on errors of the second kind, its name for a Type II error. Skip the operating characteristic curves. (reading, 4–6 min)

Optional one-stop reference (free online reference)

• American Statistical Association Releases Statement on Statistical Significance and P-Values — American Statistical Association — a short PDF news release from the professional body for statisticians. Read its numbered list of six principles for using a p-value; this course checks every p-value sentence against them. Skip the quotes around the list. (reading, 3–5 min)

Heads-up (links rot): these point to outside sites that occasionally move or rename pages. If a link ever fails, tell your instructor and use the reference above in the meantime.

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