Title: Week 11 — Readings & Resources · Confidence Intervals for a Mean
Course: Introduction to Statistics (18-Week Generic Edition)

Course: Introduction to Statistics (18-week generic edition) Objective covered: Objective 6 — construct and interpret confidence intervals for a population mean. Your primary reading is Chapter 11 (in this module — it carries the friendly t-table). Everything below is the optional, go-deeper layer.

How to use this page

Everything here is a link to an external resource — open it in your browser, the same way you'd open a YouTube link. Nothing needs to be downloaded, and nothing costs money.

The load is deliberately light: 4 short readings + 4 short videos, grouped by the four big ideas of the week. Read or watch one item per group and you're well prepared; do all of them and you'll be very comfortable. Total time is roughly 60–70 minutes if you do everything, far less if you pick one per group.

Order that matches the chapter and lecture: ① the interval idea (point estimate ± margin) → ② the t-distribution & degrees of freedom → ③ building the t-interval → ④ margin-of-error anatomy & what "95% confident" means.

A habit for this week: whenever a resource shows a finished interval, take it apart — center = (low + high)/2, margin = (high − low)/2 — and ask which multiplier the author used. If a small-sample example quietly uses 1.96 instead of a t value, you've caught the week's classic error in the wild.

① The Interval Idea: Point Estimate ± Margin

Maps to Chapter 11, Section 1 and Lecture Segments 1–2. The one-liner: an honest estimate is a range with a confidence attached — one number is a bluff.

Reading — Understanding Confidence Intervals: Easy Examples & Formulas (Scribbr) 🔗 https://www.scribbr.com/statistics/confidence-interval/ Why it's assigned: the cleanest plain-language tour of what a confidence interval is and why point estimates need cushions, with worked examples that mirror our estimate ± margin anatomy. ⏱ ~8 min

Video — Confidence Intervals, Clearly Explained!!! (StatQuest with Josh Starmer) (video, ~6 min, captioned) 🔗 https://www.youtube.com/watch?v=TqOeMYtOc1w Why it earns the click: the fastest good intuition for what an interval is — and its capture-the-truth framing is exactly the one our data lab makes you live out with your own penguin sample.

② The t-Distribution & Degrees of Freedom

Maps to Chapter 11, Section 2 and Lecture Segment 3. The hook: t is z with humility — and df = n − 1.

Reading — T-Distribution: What It Is and How To Use It (Scribbr) 🔗 https://www.scribbr.com/statistics/t-distribution/ Why it's assigned: explains why estimating σ with s earns heavier tails, how degrees of freedom index the t family, and when t effectively merges with the normal — the exact story behind our friendly table's melting column. ⏱ ~7 min

Video — Introduction to the t Distribution (non-technical) (jbstatistics) (video, ~9 min, captioned) 🔗 https://www.youtube.com/watch?v=Uv6nGIgZMVw Why it earns the click: a calm visual comparison of t curves against the z curve as df changes — you can watch the humility fade as the sample grows.

③ Building the One-Sample t-Interval

Maps to Chapter 11, Section 3 and Lecture Segment 5. The recipe: conditions → SE = s/√n → ME = t* × SE → x̄ ± ME — then say it in words.

Reading — Confidence Intervals: Interpreting, Finding & Formulas (Statistics By Jim) 🔗 https://statisticsbyjim.com/hypothesis-testing/confidence-interval/ Why it's assigned: walks the same construction we use — with a worked example on real data — and its "related functions" notes match our spreadsheet workflow; read it alongside the chapter's four-beat recipe. ⏱ ~9 min

Video — T-statistic confidence interval (Khan Academy) (video, ~12 min, captioned) 🔗 https://www.youtube.com/watch?v=hV4pdjHCKuA Why it earns the click: a full t-interval built end to end on a small sample — conditions, df, the table lookup, and the endpoints — at whiteboard pace you can pause and mirror.

④ Margin-of-Error Anatomy & What "95% Confident" Means

Maps to Chapter 11, Sections 4–5 and Lecture Segments 6–7. The two lines to keep: certainty costs width, and the 95% describes the method — 19 tickets in 20 win, and nobody gets to peek.

Reading — Margin of Error: Formula and Interpreting (Statistics By Jim) 🔗 https://statisticsbyjim.com/hypothesis-testing/margin-of-error/ Why it's assigned: takes the margin apart dial by dial — confidence level, sample size, spread — and previews how the same anatomy will read when Week 12 meets polls. ⏱ ~7 min

Video — Confidence Intervals: Crash Course Statistics #20 (CrashCourse) (video, ~13 min, captioned) 🔗 https://www.youtube.com/watch?v=yDEvXB6ApWc Why it earns the click: the liveliest treatment of the week's deepest idea — what the confidence level does and does not promise — and it polices the same two misreads our quiz loves.

Optional one-stop reference (free online text)

If you'd like one optional reference to skim, OpenStax Introductory Statistics 2e keeps its full text free to read online. Chapter 8 (Confidence Intervals) covers this week end to end — point estimates, the Student's t-distribution, and one-sample intervals for a mean. 🔗 https://openstax.org/books/introductory-statistics-2e/pages/8-introduction Why it's here: a reputable, currently-available reference you can return to when Week 12 extends the machine to proportions — entirely optional this week.

Pick-one quick path (≈25 min total)

In a hurry? You've read Chapter 11 — then do exactly these three and you'll be ready for the quiz: 1. Watch StatQuest — Confidence Intervals, Clearly Explained!!! (group ①). 2. Read T-Distribution: What It Is and How To Use It (group ②). 3. Watch Crash Course #20 — Confidence Intervals (group ④).

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 OpenStax reference above in the meantime.