Thus, estimates of correlations and effect sizes are attenuated by measurement error. Large sample size may help reduce this bias, but if the measures are of very low reliability, the analysis will be focused on random variation.
- How does sample size affect bias and variance?
- Why is a larger sample size better?
- Does a larger sample size reduce variability?
How does sample size affect bias and variance?
The size of the bias is proportional to population variance, and it will decrease as the sample size gets larger.
Why is a larger sample size better?
Larger samples more closely approximate the population. Because the primary goal of inferential statistics is to generalize from a sample to a population, it is less of an inference if the sample size is large.
Does a larger sample size reduce variability?
As the sample sizes increase, the variability of each sampling distribution decreases so that they become increasingly more leptokurtic. The range of the sampling distribution is smaller than the range of the original population.