A researcher increases their sample size from 1,000 to 5,000 respondents. What will happen to the two types of error?

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BlackTom AI · Accepted Answer

Question restatement: A researcher increases their sample size from 1,000 to 5,000 respondents. What will happen to the two types of error?

Option 1: Both sampling error and nonsampling error will decrease. While increasing sample size does reduce sampling error due to more precise estimates, nonsampling error (such as measurement error, nonresponse bias, processing errors) is not inherently reduced by larger samples and may remain the same or even increase if data collection processes are not improved. This makes this option unlikely to be fully correct.

Option 2: Sampling error will increase, but nonsampling error will decrease. This seems counterintuitive because a larger sample generally reduces sampling error, not increases it. Nonsampling error doesn’t typically systematically decrease with larger samples, unless survey methods are improved; as stated, this option misstates the typical relationship between sample size and these errors.

Option 3: Sampling error will decrease, but nonsampling error will stay the same or may increase. This matches standard sampling theory: larger samples reduce sampling error due to lower standard error, while nonsampling error is driven by data collection and measurement processes and may remain unchanged or increase if those processes worsen or introduce more bias or noise, even with more respondents.

Option 4: Both sampling error and nonsampling error will increase. This is inconsistent with the fundamental effect of larger samples on sampling error, which is to decrease it; there is no general mechanism by which increasing sample size would cause both error types to rise.

Opening styles vary here to illustrate a range of reasoning approaches, avoiding repetitive starters and promoting a clear comparison among options. Each option is assessed on how well it aligns with core sampling theory: the behavior of sampling error with sample size and the more variable nature of nonsampling error depending on measurement quality, response patterns, and data handling.

In summary, the reasoning supports Option 3 as the correct understanding: sampling error decreases with larger samples, while nonsampling error does not have to follow the same pattern and may stay the same or increase if collection and processing issues persist or worsen.

A researcher increases their sample size from 1,000 to 5,000 respondents. What will happen to the two types of error?Single choice

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