A researcher selects a random sample of 500 college students from Kentucky. It is found that 23% of students in the sample are science majors. The actual percentage of Kentucky college students that are science majors is 26%. Which type of error explains this discrepancy?

The type of error which explains this discrepancy is the sampling error. Sampling error is defined as an error caused by using or utilizing a number of samples instead of using the entire population number. In this case, the error came from the 500 Kentucky college students instead of using all Kentucky college students.

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chisnau chisnau · Answer 2 · 2019-05-31T15:35:06+02:00

Answer:

Sampling error is the answer.

Step-by-step explanation:

A researcher selects a random sample of 500 college students from Kentucky.

It is found that 23% of students in the sample are science majors but the actual percentage of Kentucky college students that are science majors is 26%.

This depicts sampling error in collecting data.

Sampling error is the reason for the difference between an estimate and the actual value of the population parameter. This error is caused by observing a sample instead of the whole population.