In your own words, explain why the hypothesis test discussed in this section is called the sign test.
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
9. Hypothesis Testing for One Sample
Steps in Hypothesis Testing
Problem 11.5.15d
Textbook Question
Performing a Runs Test In Exercises 15 – 20, (d) decide whether to reject or fail to reject the null hypothesis. Use α = 0.05
Coin Toss A coach records the results of the coin toss at the beginning of each football game for a season. The results are shown, where H represents heads and T represents tails. The coach claimed the tosses were not random. Test the coach’s claim.
H T T T H T H H T T T T H T H H

1
Step 1: Define the null and alternative hypotheses. The null hypothesis (H₀) states that the coin toss results are random, while the alternative hypothesis (H₁) states that the coin toss results are not random.
Step 2: Count the total number of runs in the sequence. A 'run' is a sequence of consecutive identical outcomes (e.g., HHH or TTT counts as one run). Identify and count the transitions between H and T to determine the number of runs.
Step 3: Calculate the expected number of runs and the standard deviation of runs under the null hypothesis. Use the formulas: E(R) = (2n₁n₂ / (n₁ + n₂)) + 1 and SD(R) = sqrt((2n₁n₂(2n₁n₂ - n₁ - n₂)) / ((n₁ + n₂)²(n₁ + n₂ - 1))), where n₁ is the number of H's and n₂ is the number of T's.
Step 4: Compute the z-score for the observed number of runs using the formula: z = (R - E(R)) / SD(R), where R is the observed number of runs, E(R) is the expected number of runs, and SD(R) is the standard deviation of runs.
Step 5: Compare the z-score to the critical z-value for a two-tailed test at α = 0.05 (z = ±1.96). If the z-score falls outside the range of -1.96 to 1.96, reject the null hypothesis. Otherwise, fail to reject the null hypothesis and conclude that the coin toss results are random.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Runs Test
The Runs Test is a non-parametric statistical test used to determine the randomness of a sequence of data points. It analyzes the occurrence of runs, which are sequences of consecutive identical elements, to assess whether the observed pattern deviates from what would be expected in a random sequence. In this context, it helps evaluate the coach's claim about the randomness of coin toss results.
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Null Hypothesis
The null hypothesis (H0) is a statement that assumes no effect or no difference, serving as a default position in hypothesis testing. In the context of the Runs Test, the null hypothesis typically posits that the sequence of coin tosses is random. The goal of the test is to determine whether there is enough evidence to reject this hypothesis in favor of an alternative hypothesis, which suggests that the sequence is not random.
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Step 1: Write Hypotheses
Significance Level (α)
The significance level (α) is a threshold used in hypothesis testing to determine whether to reject the null hypothesis. Commonly set at 0.05, it represents a 5% risk of concluding that a difference exists when there is none (Type I error). In this exercise, using α = 0.05 means that if the p-value obtained from the Runs Test is less than 0.05, the null hypothesis will be rejected, indicating that the coin toss results are likely not random.
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