A negative t-Test statistic is a numerical value that is calculated during a t-Test, which is a statistical analysis used to compare the means of two groups. The t-Test statistic indicates the likelihood that the difference between the two group means is due to chance. A negative t-Test statistic indicates that the mean of the first group is lower than the mean of the second group.
A negative t-Test statistic means that there is a significant difference between the two group means, with the first group having a lower mean than the second group. This can be interpreted as evidence that there is a true difference between the two groups and that this difference is not due to chance.
Yes, a t-Test statistic can be negative. This occurs when the mean of the first group is lower than the mean of the second group. A negative t-Test statistic is a common result and does not necessarily indicate a problem with the data or analysis.
To interpret a negative t-Test statistic, you should first consider the sample size and the level of significance used in the t-Test. If the sample size is large and the level of significance is low (e.g. p < 0.05), then a negative t-Test statistic indicates a significant difference between the two group means. If the sample size is small and the level of significance is high, a negative t-Test statistic may not be significant and may simply be due to chance.
A negative t-Test statistic may be influenced by a variety of factors, including the sample size of the groups being compared, the variability within each group, and the true difference between the two group means. It is important to consider all of these factors when interpreting a t-Test result and determining the significance of a negative t-Test statistic.