Answer:
[tex]\alpha[/tex] in statistics is set relatively low, usually at 0.05 to signify a change and to either prove or disprove a hypothesis.
The p-value however is the value that is calculated from the given sample, or population.
They relate to each other because they are indication of strength in correlation or weakness in correlation between the data.
We can say if the p-value is less than or equal to the alpha (p< 0.05), then we can reject the null hypothesis, and we say the result is statistically significant. But if it is greater we (p>0.05) we can say that there is evidence for the null hypothesis. Note: Never directly say that we "accept" it because this is just a statistical analysis.
∝, known as the significance level, is the probability that you will make the mistake of rejecting the null hypothesis when in fact it is true. The p-value measures the probability of getting a more extreme value than the one you got from the experiment.
By changing the the p-value to be greater than alpha, you accept the null hypothesis.
Hence the greater alpha the more likely you are to accept the null hypothesis even if the null hypothesis isn't correct!
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Step-by-step explanation:
