Hypothesis Testing: Analyzing The Role Of Null Hypothesis And P-Value In Statistical Inference

Slight evidence against H(0), but not enough to reject it

What’s the interpretation of a p-value between 5-10%

In statistics, the null hypothesis (H(0)) is the default hypothesis that assumes there is no significant difference between the two groups being compared. When analyzing the data using hypothesis testing, the goal is to either reject or fail to reject the null hypothesis.

If there is slight evidence against H(0), this means that the data suggests there may be some difference between the two groups being compared, but it is not enough to confidently reject the null hypothesis. In this scenario, the researcher would likely need to collect more data or perform additional statistical tests before making a definitive conclusion.

The level of evidence against the null hypothesis is typically measured by the p-value. A p-value is the probability of obtaining a test statistic as extreme as the observed statistic, assuming the null hypothesis is true. If the p-value is very small (e.g., less than 0.05), this suggests there is strong evidence against the null hypothesis, and it can be rejected. However, if the p-value is larger (e.g., between 0.05 and 0.10), this suggests there is some evidence against the null hypothesis, but it is not strong enough to reject it.

In summary, slight evidence against H(0) means that the data suggests there may be some difference between the two groups being compared, but more evidence is needed before confidently rejecting the null hypothesis. The level of evidence is typically measured by the p-value.

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