Hypothesis Testing: The Null And Alternative Hypotheses In Statistical Analysis

Strong evidence against H(0): reject it, and go with H(1)

What’s the interpretation of a p-value smaller than 1%

In hypothesis testing, the null hypothesis (H(0)) is a statement that there is no significant difference or relationship between two variables or populations. The alternative hypothesis (H(1)) is a statement that there is a significant difference or relationship between the variables or populations under study.

If there is strong evidence against H(0) from the data analysis, it means that the observed data provides more support for H(1) than H(0). In this case, we would reject the null hypothesis and accept the alternative hypothesis.

For example, suppose we conduct a study to test whether a new drug is effective in reducing the symptoms of a disease. We set up the null hypothesis that there is no significant difference in symptom reduction between the group of patients who receive the drug and the group who receive a placebo. The alternative hypothesis is that the drug significantly reduces symptoms.

After conducting the study, if we find strong evidence against the null hypothesis, such as a low p-value or a significant effect size, we would reject H(0) and support H(1) that the drug is effective in reducing symptoms.

In conclusion, strong evidence against H(0) indicates that the null hypothesis should be rejected, and we should accept the alternative hypothesis, as it provides a better explanation for the observed data.

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