There were 50 men and 50 women who were randomly selected from a larger population.
•A t-test was conducted to compare men and women’s performance on an assessment after eating chocolate. The results showed an independent t-test value of t .05(99) = 3.43; p < .05Research Report
Does this mean that the outcome was negative and the percentage of woman who performed better was 5% ?
This means that the difference between men and women was significant, P ≤ .05. There is a 5% probability that this difference is caused solely by chance (alpha error), but scientists tend to assume that chance is not acting in this case.
Why wasn't there a performance assessment on both groups before eating the chocolate to compare the changes?
The information provided in the question is not sufficient to determine whether the outcome was negative or positive. Furthermore, the statement about the percentage of women who performed better is not accurate based on the information given.
The independent t-test value, also known as t-value, in this case is 3.43. The t-value is a statistical measure used to determine if there is a significant difference between the means of two groups. In this case, the t-test was conducted to compare the performance of men and women on an assessment after eating chocolate.
However, the p-value is also important in interpreting the results of the t-test. The statement "p < .05" indicates that the p-value of the test is less than 0.05. The p-value represents the probability of observing the data or more extreme results if the null hypothesis (no difference between the groups) is true. A p-value less than 0.05 is often considered statistically significant.
To determine whether the outcome was negative or positive, we need to know the direction of the hypothesized difference between the groups. Without this information, we cannot deduce if one group (men or women) performed better or worse than the other.
Moreover, the statement about the percentage of women who performed better cannot be determined solely based on the given information. The t-value and p-value only provide information about the statistical significance of the difference between the groups, not about individual percentages or proportions.
To understand the outcome and interpret the results properly, it would be necessary to have additional information about the study design, the assessment, and the hypothesis being tested.