Practice Best Practices (4.7) - Statistical Inference and Hypothesis Testing

Practice: Best Practices

Lesson 4.7Statistical Inference and Hypothesis Testing

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10 questions

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6 sample questions

Question 1.
What is one assumption that should be verified before conducting a statistical test?
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Think about the distribution of your dataset.
Question 2.
Why is it important to report effect sizes?
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How does effect size compare to p-value?
3Question 3.
What is the primary purpose of verifying assumptions in statistical testing?
- To ensure data fits the model assumptions.
- To increase the sample size.
- To ignore outliers.
- To maximize p-values.
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Think about what assumptions are necessary for accurate testing.
4Question 4.
True or False: Effect sizes are irrelevant to hypothesis testing.
- True
- False
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Consider what additional insights effect sizes provide beyond p-values.
Question 5.
You conducted three different tests on the same dataset. The p-value for each test was 0.04. Explain how you would apply the Bonferroni correction in this scenario.
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Remember the formula for the Bonferroni correction.
Question 6.
Critically evaluate a scenario in which assuming normality might lead to a potential error in results. What would be a practical approach to handle non-normal data?
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Think about which tests are designed for non-normal conditions.

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