Here are the answers to exam 1. For some of the questions, I have provided explanatory material in regular type, and the answer in **bold**; all you need to write down is the answer. If you don't understand why your answer was wrong, you may e-mail me, talk to me before or after class, or set up a time to talk to me in my office. The exam was worth 15 points, so each question was worth 0.75 points.

**1. ** Two nominal variables, Lignextra vs. control, snoring vs. not snoring; total sample size is greater than 1000 (600+600); **chi-squared test of independence** or **G-test of independence**.

**2. ** Because the null hypothesis is true, **the probability of getting a significant result is equal to the significance level, or alpha**. Because we're using a significance level of 0.05 in this class, all you needed to write for full credit was **0.05** or **5%**.

**3. ** **Order that turtle pokes its head up: ranked
walking speed: measurementeaten vs. safe: nominal **.

**4. ** One nominal variable, dimly lit vs. dark; total sample size is less than 1000; **exact test of goodness-of-fit**.

**5. ** Two nominal variables, HTPAP genotype, cancer vs. no cancer; total sample size greater than 1000 (635+725): **chi-squared test of independence** or **G-test of independence**..

**6. ** Three nominal variables, kind of cat, adopted vs. not adopted, which shelter; **Cochran-Mantel-Haenszel test**.

**7. ** **Total number of salamanders: measurement
soil pH: measurement
number of dead logs: measurement
white oak vs. non-white oak leaf: nominal
human activity scale: measurement
amount of light: ranked **.

Whether you consider the quadrats to be a nominal variable is a gray area for this kind of experiment; we'll talk about it later in the semester. You didn't get points off for omitting or including it.

**8. ** **0.003 is the probability of getting a difference in mean milk production between grass-fed and hay-fed goats of 0.7 liters per day, or more, by chance if the null hypothesis is true**.

**9. ** **Behavior: ranked
Sugar amount: measurement
Age: measurement **

**10. ** Because 42.3% of the area is cars, ** The null hypothesis is that 42.3% of the poops will be on cars.**. There is one nominal variable, car vs. asphalt, and the total sample size is less than 1000 (61+57), so the test is **exact test of goodness-of-fit**.

**11. ** Two nominal variables, *Gpi* allele, beach; total sample size greater than 1000 (743+89+581+7): **chi-square test of independence** or **G-test of independence**.

**12. ** **Number of fireflies, measurement ** (because you don't observe the fireflies that aren't flashing, it's not the nominal variable flashing vs. non-flashing)

**sand particle size: measurement
percent of area that is bare sand: measurement ** (because it's the percentage of the area, you're not counting individual grains and sorting them into sand vs. something else).

Again, whether you consider the locations to be a nominal variable is optional.

**13. ** Two nominal variables, near vs. far from cedar tree, rust spot vs. no rust spot; total sample size less than 1000 (100+100); ** Fisher's exact test of independence**.

**14. ** One nominal variable, raccoon vs. feces vs. cheese; total sample size less than 1000 (32+19+12): **exact test of goodness-of-fit **.

The null hypothesis is that **one-third of the dogs will roll in the dead raccoon, one-third will roll in the feces, and one-third will roll in the cheese**.

**15. ** **Mannose concentration: measurement **

**oxygen content: measurement**

**weight: measurement**

**sex: nominal**

**16. ** **An exact test is better when the sample size is small, because the P-value is more accurate than for a chi-square or G-test. We use the chi-square or G-test when the sample size is large, because the calculations for an exact test are difficult even for a computer, and because all three tests give about the same P-value when sample sizes are large. **.

**17. ** Two nominal variables, species of slug, water content (because just two values, 15 and 30%); total sample size less than 1000 (50+50); **Fisher's exact test**.

**18. ** Two nominal variables, kind of corn (sweet vs. yellow dent), strain of borer (E or Z); total sample size greater than 1000 (800+940); **chi-square test of independence** or **G-test of independence**.

**19. ** **alpha: 0.05 **(also known as significance level)

**beta: 0.20** or **power: 0.80 **. You got points off for putting down both beta and power, because power equals one minus beta, you only need to pick one.

**Effect size: 15 percent fewer deaths** (the effect size for a test of independence is a percentage or proportion, *not* the number of deaths)

**20. ** ** Make alpha larger; make beta larger (or power smaller); make effect size larger.**.