How P-Values Can Help You Make Decisions in Hypothesis Testing

P-values play an important role in hypothesis testing, but using them can be tricky.

1. Misinterpretation:
   Many people misunderstand what p-values mean. A low p-value doesn't prove that your new idea (or alternative hypothesis) is correct. It only shows that the data you collected would be surprising if the old idea (or null hypothesis) was true.

2. Significance Levels:
   When you choose a significance level—usually set at 0.05—it can lead to mistakes. You might get false positives (saying a result is significant when it’s not) or false negatives (missing a real effect) if you pick these levels without careful thought.

3. Context Matters:
   P-values don’t show how important the results are in real life. This means statistical results can sometimes be misleading if you only look at p-values.

Solutions:

• Use confidence intervals along with p-values to get a better picture.
• Think about the context and combine p-values with effect sizes to understand the impact better.
• Check your results with larger sample sizes to help reduce mistakes.

By using these methods, you can make better decisions based on p-values in hypothesis testing.