Biostatistics & Research · single question

In statistical/measurement contexts, increasing the number of observations in a dataset generally has which effect on the precision of an estimate, and what can you generally NOT do to a properly randomized comparison?

You generally cannot simply increase or decrease the measured value of your outcome data after the fact to make results more favorable — only proper methodology (larger sample size, reduced variability, appropriate design) legitimately changes precision
Larger sample sizes always make results less precise
Manipulating raw data values after collection is an acceptable way to improve study power
None of the above are true

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Correct answer: A. You generally cannot simply increase or decrease the measured value of your outcome data after the fact to make results more favorable — only proper methodology (larger sample size, reduced variability, appropriate design) legitimately changes precision

A core principle of sound research methodology is that you cannot ethically alter collected outcome values to make results look better — legitimate ways to improve the precision of an estimate (e.g., narrow a confidence interval) include increasing sample size, reducing measurement variability, and using a more appropriate study design, not adjusting the data itself.

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In statistical/measurement contexts, increasing the number of observations in a dataset generally has which effect on the precision of an estimate, and what can you generally NOT do to a properly randomized comparison?

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