Try generating some data from a normal distribution, first generate a small sample and look at the spread of the points, now add a few more points, then more, then more. You will notice that as the sample size gets bigger you will see more extreme values (potential outliers) just by chance alone. If you don't do some adjustment for multiple comparisons then you will see much more significance in large sample sizes just do to the large sample size when the underlying process is stable and all the data points are legitimate (inliers?).

Try generating some data from a normal distribution, first generate a small sample and look at the spread of the points, now add a few more points, then more, then more. You will notice that as the sample size gets bigger you will see more extreme values (potential outliers) just by chance alone. If you don't do some adjustment for multiple comparisons then you will see much more significance in large sample sizes just due to the large sample size when the underlying process is stable and all the data points are legitimate (inliers?).

Try generating some data from a normal distribution, first generate a small sample and look at the spread of the points, now add a few more points, then more, then more. You will notice that as the sample size gets bigger you will see more extreeme values (potential outliers) just by chance alone. If you don't do some ajustment for multiple comparisons then you will see much more significance in large sample sizes just do to the lareg sample size when the underlying process is stable and all the data points are legitimate (inliers?).

Try generating some data from a normal distribution, first generate a small sample and look at the spread of the points, now add a few more points, then more, then more. You will notice that as the sample size gets bigger you will see more extreme values (potential outliers) just by chance alone. If you don't do some adjustment for multiple comparisons then you will see much more significance in large sample sizes just do to the large sample size when the underlying process is stable and all the data points are legitimate (inliers?).