Because it is an alternate hypothesis, the tenet "innocent until proven guilty" has application to hypothesis testing. This means that whether someone is found guilty or innocent, the opposite hypothesis will be shown to be incorrect. The innocent until proven guilty premise may be deemed undesirable in situations where statistics and probability are involved. This is because, even while there is a lot of information and other factors that point to someone doing something, this does not necessarily prove that they did it. You cannot sentence someone to prison for murder simply because the majority of the evidence is against them; this is because the evidence does not establish that they are the ones who committed the crime. Statistics may be false. This is due to the possibility that the data you collect may not come from an area that is pertinent to your hypothesis. Despite the fact that not all of the available data completely supports a person's hypothesis, some people may nevertheless make assumptions. Inaccurate results would arise from this. You can wrongly reject your null hypothesis, which is another factor.

What would be a good response to this?