Choosing different significance levels (called $\alpha$) in hypothesis testing can really change your results and the conclusions you draw.

1. Errors You Can Make:

   • If you pick a low significance level (like $\alpha = 0.01$), you lower the chance of making a Type I error. This is when you think something is true (a false positive) when it's not. So, you become more careful and want stronger proof before rejecting the null hypothesis.
   • But, using a low $\alpha$ can increase the chance of making a Type II error. This is when you miss something that is actually true (a false negative) because your rules are too strict.

2. Real-Life Examples:

   • In medical tests, if researchers use $\alpha = 0.05$, they might say a new drug works. But if they choose a stricter $\alpha = 0.01$, they could decide the drug doesn’t work, even if it really does help people.

In short, the level of $\alpha$ you choose affects your findings and how they apply in the real world.