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 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
10. Hypothesis Testing for Two Samples
Two Means - Unknown, Unequal Variance
Problem 8.2.1
Textbook Question
What conditions are necessary to use the t-test for testing the difference between two population means?

1
Ensure that the data for both populations are approximately normally distributed. This is particularly important if the sample sizes are small (n < 30). If the sample sizes are large, the Central Limit Theorem allows for some relaxation of this condition.
Verify that the samples are independent of each other. This means that the selection of one sample does not influence the selection of the other sample.
Check whether the population variances are equal or unequal. This will determine whether you use the pooled variance t-test (equal variances) or the Welch's t-test (unequal variances).
Confirm that the data is measured on an interval or ratio scale, as the t-test requires numerical data for meaningful comparison.
Ensure that the sample sizes are not too small to provide sufficient statistical power for detecting a difference, unless the effect size is expected to be large.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Normality
For the t-test to be valid, the data from both populations should ideally follow a normal distribution. This is particularly important when sample sizes are small (typically less than 30), as the t-test relies on the assumption that the sampling distribution of the mean is approximately normal.
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Independence
The observations in each sample must be independent of each other. This means that the selection of one observation should not influence the selection of another. Violating this assumption can lead to biased results and incorrect conclusions.
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Equal Variances
The t-test assumes that the variances of the two populations being compared are equal. This is known as the assumption of homogeneity of variance. If this assumption is violated, a modified version of the t-test, such as Welch's t-test, may be more appropriate.
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