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Free R statistics course

Statistical Data Analytics with R

Module 05 Knowledge Check

5 questions. Pass mark 4 out of 5. Answer every question before checking the answer key below, then retry after reading the feedback.

1. Failing to reject H0 means…

2. A p-value of 0.02 means…

3. Running twenty independent tests at alpha 0.05 when all nulls are true gives…

4. Switching to a one-sided test after seeing the direction…

5. Between a small study finding a 6.21 difference at p = 0.26 and a large one finding 0.92 at p < 0.0001…

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Answer Key and Explanations

Check these only after attempting every question.

1. B — the data was not sufficient to rule H0 out

At n = 40 with SD 15 the test could not distinguish a mean of 100 from 103.

2. B — data this extreme arises 2% of the time if H0 is true

H0 is assumed true in order to compute it, so it cannot be the probability that H0 is true.

3. B — about a 65% chance of at least one

The simulation gave 0.653 against the theoretical 1 - 0.95^20 = 0.642.

4. B — converts the false-positive rate from 5% to 10%

The one-sided p is exactly half the two-sided, so choosing the direction afterwards doubles the real error rate.

5. B — the p-value reflects sample size, not effect size

A p-value measures evidence against the null. The smaller effect had the far smaller p-value because n was 500 times larger.

Practical Check

Apply this module to your own work: complete the module activity for *Hypothesis Testing and Test Selection*, then write one sentence naming what your result shows and one naming what it does not.

Strong Answer Pattern

A strong answer names the task, the evidence used, the check performed, and the remaining limitation. It avoids "proved", "guaranteed", or "always" unless the evidence genuinely supports it.