Final answer:
A test with no validity will have a validity coefficient close to zero, indicating no significant relationship between test scores and what it aims to measure. Significance is assessed by comparing the correlation coefficient to zero, with a p-value below the alpha level (e.g., 0.05) indicating a significant relationship.
Step-by-step explanation:
If a test has no validity whatsoever, its validity coefficient would have a value close to zero. The validity coefficient measures the extent to which the test accurately reflects what it is designed to measure.
A validity coefficient close to zero suggests that there is no significant relationship between the test scores and the construct it is intended to measure.
When determining the significance of a correlation coefficient, the null hypothesis often states that there is no correlation or the correlation is zero.
If evidence suggests that the correlation coefficient is not significantly different from zero, we would fail to reject the null hypothesis, leading us to conclude that the correlation is not significant.
Conversely, if the correlation coefficient is found to be significantly different from zero, it indicates a significant relationship, prompting us to reject the null hypothesis.
An example of how significance is tested involves comparing the p-value to a predetermined alpha level (such as 0.05). If the p-value is lower than the alpha level, like 0.04 in our case, this suggests that the correlation coefficient is significant, meaning there is less than a 5% probability that the observed correlation occurred by chance under the null hypothesis.
However, it's important to check the results for reasonableness.
For instance, a validity coefficient should logically be a number between -1 and 1, where 1 signifies a perfect positive correlation, -1 signifies a perfect negative correlation, and 0 signifies no correlation at all. A validity coefficient that does not fall within this range would be an unreasonable result.