Good point. Not sure where I picked up that phrasing - especially since the wikipedia article linked by the phrase specifically says:
> It is important to understand that the null hypothesis can never be proven. A set of data can only reject a null hypothesis or fail to reject it. For example, if comparison of two groups (e.g.: treatment, no treatment) reveals no statistically significant difference between the two, it does not mean that there is no difference in reality. It only means that there is not enough evidence to reject the null hypothesis (in other words, the experiment fails to reject the null hypothesis).
I'm going to update the post to fix that mistake. Thanks.
Edit: I updated that section. This part and the next part of the series are the ones I'm most anxious about because of all the math and the questions that have right and wrong answers.
> It is important to understand that the null hypothesis can never be proven. A set of data can only reject a null hypothesis or fail to reject it. For example, if comparison of two groups (e.g.: treatment, no treatment) reveals no statistically significant difference between the two, it does not mean that there is no difference in reality. It only means that there is not enough evidence to reject the null hypothesis (in other words, the experiment fails to reject the null hypothesis).
I'm going to update the post to fix that mistake. Thanks.
Edit: I updated that section. This part and the next part of the series are the ones I'm most anxious about because of all the math and the questions that have right and wrong answers.