In a hypothesis test, standard error measures . a. variability for the sample data
b. the amount of difference expected just by chance
c. the size of the treatment effect
d. the likelihood of a Type I or Type II error

The correct answer is:

the amount of difference expected just by chance (b)

Step-by-step explanation:

Standard error in hypothesis testing is a measure of how accurately a sample distribution represents a distribution by using standard deviation. For example in a population, the sample mean deviates from the actual mean, the mean deviation is the standard error of the mean, showing the amount of difference between the sample mean and the actual mean, occurring just by chance. Mathematically standard error is represented as:

standard error = (mean deviation) ÷ √(sample size).

standard error is inversely proportional to sample size. The larger the sample size, the smaller the standard error, and vice versa.

2

step-by-step explanation: