Closed MatthewYu06 closed 1 year ago
expanded the header explaining MarkUs autotesting and marking based on updated iterations of that which were made through the first round of content creation
Q0: I need to double check that "\%" prints out correctly in MarkUs... probably does :)
Q1: minor edits
Q2: I think adding line breaks is helpful and do so here and will continue to do so
Q3: small edits
Q4: p-value calculation was accidentally one sided: I may have made this mistake in my recording
Q5: trying to move the language more towards "evidence" statements
A. $0.10 < \text{p-value}$ no evidence against the null hypothesis
B. $0.05<\text{p-value}<0.10$ weak evidence against the null hypothesis
C. $0.01<\text{p-value}<0.05$ moderate evidence against the null hypothesis
D. $0.001<\text{p-value}<0.01$ strong evidence against the null hypothesis
E. $\text{p-value}<0.001$ very strong evidence against the null hypothesis
Q5: keep the hint the same, but reword the prompt so it's just a multiple choice question (with no restated null hypothesis summary... they've already done that above so it's okay to not do it again)
test
variables for other multiple choice questions: please do that for this one as wellQ6 is fun/good
new Q7(s)[? it is probably going to be necessary to make this into multiple questions]: have students use a couple non-parametric tests and a parametric p-value calculation
scipy.stats.median_test
which assumes the medians of the two groups are identical scipy.stats.mannwhitneyu
which (more strongly) assumes "no actual difference between groups"scipy.stats.ttest_ind
which assumes the means of the two groups are identical and that the samples come from normally distributed populationshave students answer multiple choice questions about which of these are nonparametric/parametric and why: the answers should be in the homework explanation/guidance already, so these should just be a question of making some correct explanations and some mixed up explanations
I would also like a question that reminds students of the code based on scipy.stats.binom
for one-sample p-values for proportions problems; and scipy.stats.ttest_1samp
; and helps/asks them to differentiate these from the newly introduced tests up above. This should again just be a question of making some correct explanations and some mixed up explanations.
new Q8: create a confidence interval of the difference between the two groups. Guide the students to use a random number seed (just as you've done nicely in Q8) and how a two-sample interval gets constructed with bootstrapping and create an autotest to confirm this.
abs
treatment in the "as or more extreme" calculation.New questions: Q7-Q15 How to categorize evidence against null when p-value is on the boundary(0.10, 0.05, 0.01 etc.)? For now, I included the upper bound and excluded the lower (exp. 0.05 < p_value <= 0.10 is weak, and 0.01 < p_value <= 0.05 is moderate)
scipy.stats.mannwhitneyu
is different from "Wilcoxon Rank Sum test" (scipy.stats.wilcoxon
): corrected/fixed this...replace=False
to get bootstrapping... in the .sample()
method...So, then, do you have time to get this set up and running on MarkUs at this point?
made new Q18, modified Q17 answer structure, fixed general typos I caught
anxiety_data
was not defined in the autotests even though crash_data
was fine. Just ended up reassigning anxiety_data
in the Q7 autotest, I think this looks fine
Homework:
Tutorial: