Final answer:
I disagree with the statement because p-values > .10 indicates insufficient evidence to reject the null hypothesis, not an absence of effect. A p-value higher than the typical alpha level of 0.05 suggests we cannot conclude there is a statistically significant relationship with the current evidence.
Step-by-step explanation:
I disagree with the statement that performance does not have a statistically significant effect on salary once we control for experience (years) and amount played (gamesyr) simply because the p-values are > .10. The decision to disagree is based on a common standard in statistics where a significance level, typically denoted as alpha (α), is set before analyzing the data—most often at 0.05 or 5%. When a p-value is greater than the chosen alpha level, it suggests that there is insufficient evidence to reject the null hypothesis; it does not necessarily mean that there is no effect or that performance variables have no significance whatsoever
. Rather, it indicates that based on the sample data and under the 5 percent significance level, we cannot confidently state that there is a statistically significant relationship. This conclusion is aligned with various provided examples where decisions are made and reasons are given based on whether the p-value is lower or higher than the alpha level, such as whether salary and level of education are dependent or if a decrease in underemployment rates is evident.
Statistical significance is a tool to measure the strength of the evidence against the null hypothesis, not a definitive measure of truth. Hence, while these performance variables have p-values higher than .10, further investigation is recommended, possibly with more data or different model specifications, before concluding their effect on salary.