Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

Exercise 4.19 on page 269 describes a study investigating the effects of exercise on cognitive function. \({ }^{31}\) Separate groups of mice were exposed to running wheels for \(0,2,4,7,\) or 10 days. Cognitive function was measured by \(Y\) maze performance. The study was testing whether exercise improves brain function, whether exercise reduces levels of BMP (a protein which makes the brain slower and less nimble), and whether exercise increases the levels of noggin (which improves the brain's ability). For each of the results quoted in parts (a), (b), and (c), interpret the information about the p-value in terms of evidence for the effect. (a) "Exercise improved Y-maze performance in most mice by the 7 th day of exposure, with further increases after 10 days for all mice tested \((p<.01)\) (b) "After only two days of running, BMP ... was reduced \(\ldots\) and it remained decreased for all subsequent time-points \((p<.01)\)." (c) "Levels of noggin ... did not change until 4 days, but had increased 1.5 -fold by \(7-10\) days of exercise \((p<.001)\)." (d) Which of the tests appears to show the strongest statistical effect? (e) What (if anything) can we conclude about the effects of exercise on mice?

Short Answer

Expert verified
Exercise appears to improve cognitive function in mice by improving Y-maze performance, reducing levels of BMP, and increasing levels of noggin. The effect on noggin levels, which did not manifest until 4 days into exercise but had increased 1.5-fold by 7-10 days, seems to be the most statistically significant.

Step by step solution

01

Interpretation of Part (a)

In part (a) of the exercise, a p-value of less than .01 indicates strong evidence against the null hypothesis that exercise does not improve Y-maze performance in mice. Consequently, it is very likely that exercise does improve Y-maze performance, particularly after 7 or more days of exposure.
02

Interpretation of Part (b)

In part (b), a p-value of less than .01 again indicates strong evidence against the null hypothesis, in this case that exercise does not reduce levels of BMP. Especially given that this effect was observed after only two days of running and remained for all subsequent time-points, it is very likely that exercise does indeed reduce levels of BMP.
03

Interpretation of Part (c)

In part (c), a p-value of less than .001 provides extremely strong evidence against the null hypothesis that exercise does not increase the levels of noggin. Given that this effect did not change until 4 days but had increased 1.5-fold by 7-10 days of exercise, we can conclude it is highly likely that exercise increases the levels of noggin, but only after a certain time point.
04

Determine the Strongest Statistical Effect

Given that the p-value for the effect on noggin levels was the smallest, part (c) appears to show the strongest statistical effect.
05

Conclusion on the Effects of Exercise on Mice

Given the results from parts (a), (b), and (c), it seems quite likely that exercise improves brain function in mice by boosting Y-maze performance, reducing levels of BMP, and increasing levels of noggin. However, the exact mechanism and timescales for these effects warrant further investigation.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Give null and alternative hypotheses for a population proportion, as well as sample results. Use StatKey or other technology to generate a randomization distribution and calculate a p-value. StatKey tip: Use "Test for a Single Proportion" and then "Edit Data" to enter the sample information. Hypotheses: \(H_{0}: p=0.7\) vs \(H_{a}: p<0.7\) Sample data: \(\hat{p}=125 / 200=0.625\) with \(n=200\)

Utilizing the census of a community, which includes information about all residents of the community, to determine if there is evidence for the claim that the percentage of people in the community living in a mobile home is greater than \(10 \%\).

Flying Home for the Holidays, On Time In Exercise 4.115 on page \(302,\) we compared the average difference between actual and scheduled arrival times for December flights on two major airlines: Delta and United. Suppose now that we are only interested in the proportion of flights arriving more than 30 minutes after the scheduled time. Of the 1,000 Delta flights, 67 arrived more than 30 minutes late, and of the 1,000 United flights, 160 arrived more than 30 minutes late. We are testing to see if this provides evidence to conclude that the proportion of flights that are over 30 minutes late is different between flying United or Delta. (a) State the null and alternative hypothesis. (b) What statistic will be recorded for each of the simulated samples to create the randomization distribution? What is the value of that statistic for the observed sample? (c) Use StatKey or other technology to create a randomization distribution. Estimate the p-value for the observed statistic found in part (b). (d) At a significance level of \(\alpha=0.01\), what is the conclusion of the test? Interpret in context. (e) Now assume we had only collected samples of size \(75,\) but got essentially the same proportions (5/75 late flights for Delta and \(12 / 75\) late flights for United). Repeating steps (b) through (d) on these smaller samples, do you come to the same conclusion?

In a test to see whether there is a difference between males and females in average nasal tip angle, the study indicates that " \(p>0.05\)."

Data 4.2 on page 263 describes a study of a possible relationship between the perceived malevolence of a team's uniforms and penalties called against the team. In Example 4.36 on page 326 we construct a randomization distribution to test whether there is evidence of a positive correlation between these two variables for NFL teams. The data in MalevolentUniformsNHL has information on uniform malevolence and penalty minutes (standardized as \(z\) -scores) for National Hockey League (NHL) teams. Use StatKey or other technology to perform a test similar to the one in Example 4.36 using the NHL hockey data. Use a \(5 \%\) significance level and be sure to show all details of the test.

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free