The Central Limit Theorem (CLT) tells us something important about statistics:

• When we have a big enough sample size, usually at least 30, the average of those samples will behave like a normal distribution, no matter what the original data looks like.
• This normal behavior is useful because it lets us use certain tests that work best with normally distributed data.

Here’s how this impacts hypothesis testing:

1. We can calculate p-values using the normal distribution.
2. We can create confidence intervals with the following formula:
   $\bar{x} \pm z_{\alpha/2} \frac{\sigma}{\sqrt{n}}$
3. Tests like t-tests are strong and can still be used even if we don't know what the original data looks like, as long as our sample size is large enough.

In short, the Central Limit Theorem helps make hypothesis testing easier because it allows us to use normal distribution rules in statistics.