Because our p-value is GREATER than alpha…
… we fail to reject the null. There is NOT sufficient evidence to conclude that (state null hypothesis in context/answer the question).
We fail to reject the null hypothesis.
In hypothesis testing, the p-value measures the probability of obtaining a sample statistic as extreme as the one observed or more extreme, assuming the null hypothesis is true. On the other hand, alpha (α) is the significance level, which represents the maximum probability of rejecting the null hypothesis when it is really true.
If the computed p-value is greater than alpha, it means that the observed sample statistics are not significant enough to reject the null hypothesis. In other words, the evidence does not provide enough support to deviate from the null hypothesis assumption.
Therefore, we would fail to reject the null hypothesis and accept the possibility that our results occurred simply by chance or noise and do not represent a true difference between the population and the sample. It is essential to understand that this does not prove that the null hypothesis is unequivocally true. Instead, it merely indicates that we cannot make a definite decision in this situation based on the results obtained in the experiment.
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