StatQuest: One or Two Tailed P-Values

Summary

In this Stat Quest, brought to you by the Genetics Department at UNC Chapel Hill, the focus is on one-tailed vs. two-tailed tests in statistics. Using a cancer treatment trial as an example, the video explains that a one-tailed test shows a p-value of 0.03, indicating significance, while a two-tailed test shows 0.06, not significant. The importance of choosing the test type before the experiment to avoid false positives is emphasized. The video concludes that a two-tailed test is generally preferable to get a complete picture of the data.

Takeaways

• 📊 One-tailed tests focus on whether one treatment is better than another, while two-tailed tests evaluate if there is any difference, either better or worse.
• 🔬 In clinical trials, it's crucial to decide on the type of test and p-value threshold before conducting the experiment to avoid bias.
• 📉 A one-tailed test may produce a smaller p-value because it doesn't account for the possibility of the new treatment being worse.
• ⚖️ Good statistical practice involves using a two-tailed test to get a complete picture of the treatment's effectiveness.
• 🛑 Waiting to choose the type of test until after seeing the data can lead to p-hacking and false positives.
• 🔍 A false positive occurs when a test indicates a significant result when there is none, which can be influenced by the type of test used.
• 📈 Using a one-tailed test inappropriately can increase the probability of reporting false positives from 5% to 8%.
• 💡 Always aim to understand both sides of the data story, not just the side that seems favorable.
• 🔎 Some statistical tests do not offer a choice between one-tailed and two-tailed, but when given a choice, the two-tailed test is generally preferable.
• 🎉 The end message is clear: for accurate and unbiased results, always use a two-tailed test when you have the option.

Q & A

What is the primary topic discussed in the transcript?

The primary topic discussed is the difference between one-tailed and two-tailed tests in statistical analysis and when to use each type.

Why might someone choose to use a one-tailed test?

A one-tailed test is used when the hypothesis specifically predicts that one treatment or condition will have a better outcome than another.

What p-value did the one-tailed test yield in the example given?

The one-tailed test yielded a p-value of 0.03.

What p-value did the two-tailed test yield in the example given?

The two-tailed test yielded a p-value of 0.06.

Why is the two-tailed p-value generally preferred over the one-tailed p-value?

The two-tailed p-value is preferred because it tests whether the new treatment is better, worse, or not significantly different from the standard treatment, providing a more comprehensive analysis.

What is the risk associated with deciding the type of test after seeing the data?

Deciding the type of test after seeing the data can lead to 'p-hacking,' increasing the probability of reporting false positives.

What is a false positive in the context of statistical testing?

A false positive occurs when the test indicates a significant effect when there is none, typically expected to happen 5% of the time in a two-tailed test.

How did the false positive rate change when switching to a one-tailed test after seeing favorable data?

The false positive rate increased from 5% to 8% when switching to a one-tailed test after seeing favorable data.

What lesson does the transcript emphasize regarding the choice of statistical tests?

The transcript emphasizes the importance of deciding which statistical test to use before conducting the experiment to avoid p-hacking and ensure accurate results.

What is the recommended practice when you have a choice between a one-tailed and a two-tailed test?

The recommended practice is to always choose a two-tailed test to fully understand both sides of the data and avoid biased results.