To understand which error is likely to occur when a researcher finds evidence to reject the null hypothesis using a parametric test, let's examine the concept of Type I (Alpha) and Type II (Beta) errors:

1. The null hypothesis is the default assumption that there is no effect or no difference in the context of the study.
2. Type I Error (or Alpha Error): This occurs when the null hypothesis is true, but we incorrectly reject it. Essentially, we detect an effect or difference that doesn't actually exist. The probability of committing a Type I error is denoted by \alpha, which is also known as the significance level of the test.
3. Type II Error (or Beta Error): This takes place when the null hypothesis is false, but we fail to reject it. This means we miss detecting an actual effect or difference. The probability of committing a Type II error is denoted by \beta.
4. Sampling Error: This refers to the error caused by observing a sample instead of the whole population, leading to differences between the sample statistic and the actual population parameter.
5. Non-Responsive Error: This error occurs when responses are not obtained from all selected respondents, potentially leading to biased estimates.

Given these definitions, when a researcher finds evidence to reject the null hypothesis using a parametric test, they run the risk of making a Type I Error (rejecting a true null hypothesis). Thus, the correct answer is:

Alpha Error

Rejecting the null hypothesis when it is actually true is precisely what defines an Alpha Error. Therefore, in the context of hypothesis testing, if evidence suggests rejecting the null hypothesis, Alpha Error is likely to happen.