To reduce the chances of making mistakes in hypothesis testing, researchers can use these simple strategies:

1. Choose the Right Significance Level ($\alpha$):

   • Usually, researchers set the significance level at 0.05. If you lower this number, it helps reduce the chances of a Type I error (wrongly finding a result) but might raise the chances of a Type II error (missing a real result).

2. Increase Sample Size ($n$):

   • Using a bigger group of subjects or data points makes the test stronger. This means there’s a lower chance of making a Type II error.

3. Use Power Analysis:

   • Power analysis helps figure out how many subjects are needed. It finds a good balance between the risks of Type I and Type II errors.

4. Pre-register Your Plans:

   • Writing down your hypotheses and analysis plans before starting reduces the chance of looking for data that supports your ideas (this is called data dredging). It helps keep the results honest and reduces the risk of Type I errors.

By carefully using these strategies, researchers can make better and more reliable conclusions from their tests.