Statistical Learning: 13.R.1 Bonferroni and Holm | Summary and Q&A

TL;DR

Simulated data is used to demonstrate the process of multiple testing and hypothesis testing, with a focus on controlling the family-wise error rate using methods like Bonferroni and Holm.

Key Insights

• ❓ Simulated data can be used to analyze the accuracy of hypothesis testing procedures.
• 😀 The p-value indicates the strength of evidence against the null hypothesis.
• ☠️ Controlling the family-wise error rate is essential to avoid falsely rejecting multiple null hypotheses.
• ☠️ Different methods like Bonferroni and Holm can be applied to adjust p-values and control the family-wise error rate.
• 🧑‍🏭 The choice between methods depends on factors like desired significance level and the specific hypothesis being tested.
• 🎚️ The choice of significance level is arbitrary, but it influences the number of rejections and false rejections.
• ⚾ The number of rejections and false rejections can vary based on the strength of evidence in the data.

Transcript

all right well welcome back uh we're now gonna walk through the the lab on multiple testing and of course the first thing that we do whenever we sit down at our computers is that we should set a random seed so that um you'll get the same results that we get so here i see the six and now we're going to simulate some data and the data is going to cor... Read More

Questions & Answers

Q: Why is it necessary to set a random seed before simulating the data?

Setting a random seed ensures reproducibility, so that others can obtain the same results when working with the data. It eliminates variations caused by random number generation.

Q: How are the p-values used to determine if the null hypothesis should be rejected?

If the p-value is less than the significance level (usually 0.05), the null hypothesis is rejected. A lower p-value suggests stronger evidence against the null hypothesis.

Q: What is the significance of the two by two table created?

The table provides insights into false rejections (Type I errors), successful rejections, and the overall ability to reject or accept the null hypothesis. It helps evaluate the accuracy and reliability of the testing process.

Q: Why is the ability to reject the null hypothesis affected by the difference between the mean and the null hypothesis?

When the difference is small, there might not be enough evidence in the data to reject the null hypothesis. The greater the difference, the stronger the signal, making it easier to reject the null hypothesis.

Summary & Key Takeaways

• Simulated data is generated for 100 hypothesis tests, with each test aiming to determine if the mean of a specific vector is equal to 0.

• The t.test command is used to test the null hypothesis for each column, with the p-value indicating if the null hypothesis can be rejected.

• A two by two table is created to analyze the results, with insights into false rejections, successful rejections, and the strength of the signal in the data.