When conducting a hypothesis test, it is essential to interpret the results correctly to draw meaningful conclusions. The two key components in interpreting hypothesis test results are p-values and confidence intervals.

P-Values: The p-value represents the probability of obtaining the observed data, or data more extreme, assuming that the null hypothesis is true. A p-value less than the chosen significance level (usually 0.05) indicates strong evidence to reject the null hypothesis in favor of the alternative hypothesis. On the other hand, a p-value greater than the significance level suggests insufficient evidence to reject the null hypothesis.

Confidence Intervals: Confidence intervals provide a range of plausible values for the population parameter. A 95% confidence interval implies that if the same sample were to be taken repeatedly, the calculated confidence interval would encompass the true population parameter in approximately 95% of the cases.

It is crucial to remember that statistical significance does not necessarily imply practical significance. Even if a result is statistically significant, it may not have much practical importance. Always consider the context and practical implications of the finding.