Final answer:
Hypothesis testing is the statistical concept likely to be used to make inferences about the distribution parameters based on a sample of exam completion times.
Step-by-step explanation:
The statistical concept likely to be used to make inferences about the distribution parameters based on a sample of exam completion times is hypothesis testing.
By collecting a sample of exam completion times and analyzing the data, we can perform hypothesis tests to make conclusions about the population mean and standard deviation. This involves setting up null and alternative hypotheses, calculating test statistics, and determining p-values to evaluate the strength of evidence against the null hypothesis.
For example, in the given scenario, a t-test or z-test could be used to test if the sample mean is significantly different from a hypothesized population mean.
The statistical concept likely to be used to make inferences about the distribution parameters based on a sample of exam completion times is called hypothesis testing. This is when statistics come into play to estimate the unknown parameters of the population (mean and standard deviation) from a sample, especially when the population's standard deviation is unknown.
In such a scenario, a Student's t-distribution is often used. One would also verify assumptions like the normality of the distribution and the randomness of the sample, ensuring that the conditions for the central limit theorem are met or that the population was originally normally distributed for the t-distribution to be applicable.