Besides a high alpha level, which factor increases the risk of a Type I error?

Besides a high alpha level, which factor increases the risk of a Type I error?

• A. Many tests are conducted
• B. Using a very large sample
• C. Low variance in data
• D. High statistical power

Answer: (A)

When you run many statistical tests, the chance of finding at least one significant result by luck alone increases. Each test has its own risk of a false positive equal to the alpha level. So, if you conduct multiple tests, the overall probability that at least one test crosses the significance threshold by chance grows. For example, with five tests at alpha 0.05, the chance of at least one false positive is about 23%; with twenty tests, it jumps to around 64%. This is the multiple comparisons problem: more tests mean higher risk of Type I errors overall. The other factors described affect power or precision rather than the rate of false positives across tests. A very large sample boosts power, not the per-test error rate. Low variance tightens the data spread, which also increases power. High power means you’re better at detecting true effects, not necessarily more false positives per test. To guard against multiple comparisons, researchers adjust significance levels or p-values (e.g., Bonferroni or FDR procedures) or limit the number of planned tests.

When you run many statistical tests, the chance of finding at least one significant result by luck alone increases. Each test has its own risk of a false positive equal to the alpha level. So, if you conduct multiple tests, the overall probability that at least one test crosses the significance threshold by chance grows. For example, with five tests at alpha 0.05, the chance of at least one false positive is about 23%; with twenty tests, it jumps to around 64%. This is the multiple comparisons problem: more tests mean higher risk of Type I errors overall.

The other factors described affect power or precision rather than the rate of false positives across tests. A very large sample boosts power, not the per-test error rate. Low variance tightens the data spread, which also increases power. High power means you’re better at detecting true effects, not necessarily more false positives per test. To guard against multiple comparisons, researchers adjust significance levels or p-values (e.g., Bonferroni or FDR procedures) or limit the number of planned tests.