Consider the possibility that you will be charged with shoplifting. You are put on trial even if you are aware that you are innocent and must demonstrate it. The legal maxim "innocent until proven guilty" applies in this situation. The prosecution has the burden of proving beyond a reasonable doubt that you did take the property. Similar to this, the null hypothesis in a hypothesis test implies no difference or change until sufficient data point to the contrary. Consider that you are a scientist working on a novel medication. Once you have enough data to reject this hypothesis and come to the conclusion that the medication does have an effect, the null hypothesis would be that the medication only has an influence on the patients. One potential drawback of this theory is that guilty parties may only escape punishment if the burden of proof is met. Similar to this, rejecting the null hypothesis when it is true might result in erroneous conclusions and wasted chances for additional research or action. As a result, it is crucial to carefully assess the quantity of data needed to disprove the null hypothesis and base decisions on the findings of hypothesis testing.

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