Question

When constructing and implementing hypothesis tests, what reasoning is used behind the statement of the null and alternative hypotheses? Why are hypothesis tests set up in this way?

Answer 1

A null hypothesis states there is no statistical significance between the two variables and alternative hypothesis is the one that states there is a statistically significant relationship between the variables.In Constructing Hypothesis, we want to test the claim, and for every test, they are two sides, whether we accept the claim or found no insufficient evidence to accept the claim, and thus hypothesis helps us to choose the decision based on the calculations.

For every hypothesis testing we test a claim considering a null and an alternative hypothesis.Here, sample data is evaluated to get a decision about the type of claim. If certain conditions about the sample are satisfied, then the claim can be evaluated for a population. And a hypothesis is set up to reject the null hypothesis. And rejecting null means the variables are significantly different from each other. This is the reason behind the statement of null and alternative hypothesis.

In hypothesis testing , test statistic evaluates a claim about a population by the sample data. And of course a test or anything we do is to get a significant outcome. So, the test is designed in such a way that the statistician tries to reject the insignificant outcome by rejecting null hypothesis and otherwise we say that due to lack of evidence we fail to reject the null hypothesis. Since, we are not studying the whole population and studying just a part of it (sample). So, there's always a chance of getting errors.And so we say, we fail to reject the null hypothesis in such cases. That's why hypothesis is set up in this way.

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