Key takeaway: The lower your alpha, the less likely you are to commit a type 1 error, but the higher the risk of missing real effects (type 2 errors).
2. Power of the test (1 – beta)
its ability to detect a real effect when one exists.
A higher power reduces the probability of committing a type II иран номера телефонов error. Factors like sample size, effect size, and variance all influence statistical power.
Key takeaway: The more power your test has, the less likely you will miss a meaningful test result.
How to avoid type 1 errors?
Let’s dive into the strategies for minimizing type 1 errors and making sure you’re not acting on false positives.
1. Set an appropriate alpha level
Choosing the right alpha level depends on your context. For example, in medical research, where the consequences of type 1 errors are serious, a lower alpha (e.g., 0.01) is more appropriate.
In contrast, digital marketers might be more comfortable with a standard alpha of 0.05, since the cost of a false positive may not be as severe.
Pro tip: If you want to play it safe, consider lowering your alpha, but be mindful of the trade-offs.
2. Use proper experimental design
A well-designed experiment is your first line of defense against type 1 errors.
