An addictions researcher measured tolerance to alcohol in first-year and fourth-year college students. She gave participants a standard dose of alcohol and then had them walk along a narrow line painted on the floor. The higher the percentage of the distance that they were on the line, the greater their tolerance to alcohol. The researcher expected that the older students would show more tolerance to alcohol. Here is the relevant information: 1st year (control): M = 30, s = 12.5, n = 20. 4th year (experimental): M = 48, s = 14.6, n = 16. Use α = .05. 10. Pick a test that’s appropriate for this study. 11. Explain why the statistical test is appropriate. 12. List the hypotheses
Contingency Table
A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.
Binomial Distribution
Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.
An addictions researcher measured tolerance to alcohol in first-year and fourth-year college students. She gave participants a standard dose of alcohol and then had them walk along a narrow line painted on the floor. The higher the percentage of the distance that they were on the line, the greater their tolerance to alcohol. The researcher expected that the older students would show more tolerance to alcohol. Here is the relevant information: 1st year (control): M = 30, s = 12.5, n = 20. 4th year (experimental): M = 48, s = 14.6, n = 16. Use α = .05.
10. Pick a test that’s appropriate for this study.
11. Explain why the statistical test is appropriate.
12. List the hypotheses
13. Set the decision rule
14. Calculate the test statistic
15. Estimate the magnitude of the effect size (Cohen’s d).
16. Calculate the 95% confidence interval for the differences between population means.
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