Final answer:
The statement that performance statistics from a support group are not useful for evaluating individual employees is true. Individual performance should be assessed with specific individual metrics, not group averages. The use of non-representative samples or small sample sizes can lead to inaccurate population inferences in statistical analysis.
Step-by-step explanation:
Performance statistics collected and analyzed for a support group could be misleading when used to evaluate individual support employees. This statement is true, as the collective data for a group does not necessarily reflect the performance of individuals within the group. Evaluations of individual employees should be based on individual performance metrics to ensure accuracy and effectiveness.
When considering experimental design in statistics, several points need to be addressed, such as the significance level and sample representativeness. A 5 percent level of significance sets a standard for determining if observations in the sample data reflect actual changes or random chance. At this level, if there isn't sufficient evidence to conclude an effect, it likely means the results were not statistically significant.
As for the representativeness of samples, it is generally not wise to use results from non-representative samples to describe the whole population. Doing so can lead to inaccurate conclusions and poor decision-making. Larger sample sizes are often better as they can provide more accurate estimates of population parameters. However, even with larger samples, careful attention must be given to whether the sample is representative of the population of interest.
It is also a misconception that data not supporting a hypothesis is not useful. Such data is often invaluable as it can guide researchers towards modifying the hypothesis or understanding the limitations of their research. Similarly, experimentation is just one of many types of scientific investigation, and observational studies or theoretical work can also provide valid scientific insights.