A large sample reduces the variability of the estimate. The extent to which variability is reduced depends on the quality of the sample, what variable is being estimated and the underlying distribution for that variable.
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The sample size has no effect on the validity of an experiment: instead, it is the experimental procedure and integrity of the experimenters.The sample size can affect conclusions that may be drawn from an experiment. The larger the sample is, the more reliable these conclusions are.
Random Sampling increases the reliability and validity of your research findings. To begin with, Reliability: By randomly picking research participants, the likelihood that they are from different backgrounds/ have different experiences etc. is higher and hence, they are said to be more representative of the population of interest. EG: RQ: Do females have higher IQ? A case of random sampling will pick females who are housewives/ CEOs/ Indian/ 18yrs old/ Divorced etc. the list goes on. While a case of non-random sampling (such as picking participants at a bus stop) may only result in a sample of females who are 20 - 35 years old, working professionals. Validity: As reliability and validity are related, for the research findings to be reliable and generalizable to the population of interest, it first has to be a valid sample. Hence, from the above example, EG1 provides a valid sample, while EG2 is invalid.
Assuming that the population was carefully defined, the sample population was carefully and correctly chosen, and that there were significant results, then the implication is that the results of the study, within the confidence limits indicated, hold true for the population at large.
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The larger the sample size, the more accurate the test results.