Four different high schools in local towns took random samples of 100 students in three grades, and collected data on the weekly time spent studying to see if students in each of these grades study on average for the same amount of time per week. The four schools ran ANOVA tests on their samples, and the F-Statistics were , , , and . Which F-Statistic is most likely to indicate the average study times across grades are not all the same?
Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 53m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample2h 19m
- 10. Hypothesis Testing for Two Samples3h 22m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
14. ANOVA
Introduction to ANOVA
Problem 10.RE.12
Textbook Question
In Exercises 9–12, find the critical F-value for a right-tailed test using the level of significance α and degrees of freedom d.f.N and d.f.D.
α=0.05,d.f.N=20,d.f.D=25

1
Step 1: Understand the problem. You are tasked with finding the critical F-value for a right-tailed test using the given level of significance (α = 0.05) and degrees of freedom for the numerator (d.f.N = 20) and denominator (d.f.D = 25). The critical F-value is the value that separates the rejection region from the non-rejection region in an F-distribution.
Step 2: Recall the formula and concept. The F-distribution is used in hypothesis testing, particularly in ANOVA and regression analysis. The critical F-value depends on the level of significance (α), the degrees of freedom for the numerator (d.f.N), and the degrees of freedom for the denominator (d.f.D).
Step 3: Use an F-distribution table or statistical software. Locate the row corresponding to d.f.N = 20 and the column corresponding to d.f.D = 25 in the F-distribution table for α = 0.05. Alternatively, use statistical software like R, Python, or Excel to compute the critical F-value.
Step 4: Interpret the result. The critical F-value represents the threshold above which the test statistic would lead to rejecting the null hypothesis in a right-tailed test. Ensure you understand its role in hypothesis testing.
Step 5: Verify your result. Double-check the table or software output to ensure accuracy. If using software, confirm that the inputs (α, d.f.N, d.f.D) are correctly entered.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Critical F-value
The critical F-value is a threshold used in hypothesis testing to determine whether to reject the null hypothesis in an F-test. It is derived from the F-distribution, which is used to compare variances between two groups. The critical value is based on the chosen significance level (α) and the degrees of freedom for the numerator (d.f.N) and denominator (d.f.D). If the calculated F-statistic exceeds this critical value, the null hypothesis is rejected.
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Degrees of Freedom
Degrees of freedom (d.f.) refer to the number of independent values or quantities that can vary in an analysis without violating any constraints. In the context of an F-test, d.f.N represents the degrees of freedom associated with the numerator (typically the group with more variance), while d.f.D represents the degrees of freedom for the denominator (the group with less variance). These values are crucial for determining the shape of the F-distribution and finding the critical F-value.
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Significance Level (α)
The significance level (α) is the probability of rejecting the null hypothesis when it is actually true, also known as a Type I error. It is a threshold set by the researcher, commonly at 0.05, which indicates a 5% risk of concluding that a difference exists when there is none. In hypothesis testing, the significance level helps determine the critical value needed to assess the results of the test, guiding the decision-making process regarding the null hypothesis.
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