Explain how to use a t-test to test a hypothesized mean mu when sigma is unknown. What assumptions are necessary?
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 26m
- 11. Correlation1h 6m
- 12. Regression1h 35m
- 13. Chi-Square Tests & Goodness of Fit1h 57m
- 14. ANOVA1h 0m
9. Hypothesis Testing for One Sample
Steps in Hypothesis Testing
Problem 11.R.13de
Textbook Question
In Exercises 13 and 14, (d) decide whether to reject or fail to reject the null hypothesis, and (e) interpret the decision in the context of the original claim.
Use
[APPLET] A highway patrol officer stops speeding vehicles on an interstate highway. The genders of the last 25 drivers who were stopped are shown, where F represents a female driver and M represents a male driver. Can you conclude that the stops were not random by gender?
F M M M F M F M F F F M M
F F F M M M F M M F F M

1
Step 1: Define the null hypothesis (H₀) and the alternative hypothesis (H₁). H₀: The stops are random by gender (proportion of males and females is equal). H₁: The stops are not random by gender (proportion of males and females is not equal).
Step 2: Calculate the observed frequencies of males (M) and females (F) from the data. Count the number of M's and F's in the given sequence.
Step 3: Determine the expected frequencies under the null hypothesis. If the stops are random, the expected frequency for each gender is (total number of stops) × (0.5), assuming equal probability for males and females.
Step 4: Perform a chi-square goodness-of-fit test. Use the formula χ² = Σ((Oᵢ - Eᵢ)² / Eᵢ), where Oᵢ is the observed frequency and Eᵢ is the expected frequency for each category (male and female).
Step 5: Compare the calculated χ² value to the critical value from the chi-square distribution table at the appropriate significance level (e.g., α = 0.05) and degrees of freedom (df = number of categories - 1). Decide whether to reject or fail to reject H₀ based on this comparison, and interpret the result in the context of the problem.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Null Hypothesis
The null hypothesis is a statement that there is no effect or no difference, and it serves as the default assumption in hypothesis testing. In this context, it posits that the gender distribution of the stopped drivers is random, meaning that the proportion of male and female drivers stopped is consistent with the overall population. Testing this hypothesis involves statistical analysis to determine if observed data significantly deviates from what would be expected under this assumption.
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Step 1: Write Hypotheses
Statistical Significance
Statistical significance refers to the likelihood that a relationship observed in data is not due to random chance. In hypothesis testing, a result is considered statistically significant if the p-value is less than a predetermined threshold (commonly 0.05). This concept is crucial for deciding whether to reject the null hypothesis, as it indicates whether the observed gender distribution in the stops is unlikely to have occurred if the null hypothesis were true.
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Interpretation of Results
Interpreting results involves explaining the implications of the statistical analysis in the context of the original claim or research question. After deciding whether to reject or fail to reject the null hypothesis, it is essential to articulate what this decision means in practical terms. For instance, if the null hypothesis is rejected, it suggests that the stops were not random by gender, which could have implications for law enforcement practices and gender bias.
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