Need help with questions 18 to 22 A researcher is interested in studying how caf

Question

Need help with questions 18 to 22 A researcher is interested in studying how caffeine intake affects test performance, and states his research hypothesis H_1: Consuming caffeine will improve test scores What kind of hypothesis test would the researcher use? a. One-tailed hypothesis test. (Right) b. One-tailed hypothesis test. (Left) c. Two-tailed hypothesis test. d. There's not enough information. In a normal distribution _____ % of all data is within 1 SD from the mean a. 68% b. 95% c. 99.7% d. 100% In a sample distribution, the mean of the sample is equal to the population mean a. True b. False When two probabilities add up to 1, they are called a. Complimentary b. Supplementary c. Elementary d. Complementary According to the Central Limit Theorem, as sample size increases, the sampling distribution becomes _____ in shape. a. Normal b. Skewed c. Flat d. Bimodal

Explanation / Answer

18.(a) One tailed hypothesis (Right)

Because - A right tailed test (sometimes called an upper test) is where your hypothesis statement contains a greater than (>) symbol. In other words, the inequality points to the right. For example, you might be comparing the life of batteries before and after a manufacturing change. If you want to know if the battery life is greater than the original (let’s say 90 hours), your hypothesis statements might be:
Null hypothesis: No change (H0 = 90).
Alternate hypothesis: Battery life has increased (H1) > 90.

19. (a) 68%

These facts are what is called the 68 95 99.7 rule, sometimes called the Empirical Rule.

20. (a) True

The mean of the sampling distribution (x) is equal to the mean of the population (). And the standard error of the sampling distribution (x) is determined by the standard deviation of the population (), the population size (N), and the sample size (n). These relationships are shown in the equations below:

x =      and      x = [ / sqrt(n) ] * sqrt[ (N - n ) / (N - 1) ]

In the standard error formula, the factor sqrt[ (N - n ) / (N - 1) ] is called the finite population correction or fpc. When the population size is very large relative to the sample size, the fpc is approximately equal to one; and the standard error formula can be approximated by:

x = / sqrt(n).

21. (d) Complementary

In complementary probability, two probabilities add up to 1.

21. (a) Normal

As we randomly select more and more samples from the population, the distribution of sample means becomes more normally distributed and looks looks smoother.

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