the sample size in research must be?
Answers
Answer:
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Explanation:
The sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. ... The sample must also be adequate in size – in fact, no more and no less...
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Explanation:
Sample size refers to the number of participants or observations included in a study. This number is usually represented by n. The size of a sample influences two statistical properties: 1) the precision of our estimates and 2) the power of the study to draw conclusions.
To use an example, we might choose to compare the performance of marathon runners who eat oatmeal for breakfast to the performance of those who do not. Since it would be impossible to track the dietary habits of every marathon runner in the world, we have little choice but to focus on a segment of that larger population. This might mean randomly selecting only 100 runners for our study. The sample size, or n, in this scenario is 100.
The study’s findings could describe the population of all runners based on the information obtained from the sample of 100 runners. No matter how careful we are about choosing our 100 runners, there will still be some margin of error in the study results. This is because we haven’t talked to everyone in our population of interest. We can’t be absolutely precise about how eating oatmeal affects running performance because it would be impossible to look at every instance in which these two activities coincide. This measure of error is known as sampling error. It influences the precision of our description of the population of all runners.
Sampling error, though unavoidable, can be eased by sample size. Larger samples tend to be associated with a smaller margin of error. This makes sense. To get an accurate picture of the effects of eating oatmeal on running performance, we need plenty of examples to look at and compare. However, there is a point at which increasing sample size no longer impacts the sampling error. This phenomenon is known as the law of diminishing returns.