Final answer:
In addition to mean values and p-values, reporting confidence intervals, effect sizes, and standard deviations is essential to understand the results of tests where group differences are measured.
Step-by-step explanation:
When reporting results where group differences are measured, it's essential to report several statistics to fully understand the results. In addition to standard statistics like mean values and p-values, reporting effect sizes, confidence intervals, and standard deviations gives a more complete picture of the data. Confidence intervals provide a range of values that are likely to include the population parameter with a certain level of confidence, reflecting the precision of the estimate. Effect sizes offer insight into the magnitude of the difference, which is crucial for interpreting the practical significance of the findings. Reporting standard deviations helps understand the variability within each group.
In the specific example where a powder diet and a liquid diet are tested on different groups of people with known population standard deviations, conducting an appropriate hypothesis test, such as a two-sample t-test, would be suitable assuming normal distribution of the data. If the p-value from this test is below the significance level, typically 0.05, we would reject the null hypothesis, indicating there is a statistically significant difference in mean weight loss between the two diets.