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The null hypothesis is a statement that assumes there is no significant difference between two groups or variables being compared.

In hypothesis testing, the null hypothesis is the default assumption that there is no significant difference between the two groups being compared. It is often denoted as H0. The alternative hypothesis, denoted as Ha, is the opposite of the null hypothesis and assumes that there is a significant difference between the two groups.

For example, if we are testing whether a new drug is effective in treating a certain disease, the null hypothesis would be that the drug has no effect, while the alternative hypothesis would be that the drug does have an effect.

The null hypothesis is tested using statistical methods, such as a t-test or chi-squared test, to determine the probability of obtaining the observed results if the null hypothesis were true. If the probability is very low (usually less than 5%), we reject the null hypothesis and accept the alternative hypothesis.

It is important to note that failing to reject the null hypothesis does not necessarily mean that the null hypothesis is true. It simply means that there is not enough evidence to reject it. Therefore, it is always important to consider the context and limitations of the study when interpreting the results.

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