Which error is increased by increasing sample size?
Type II error
As the sample size increases, the probability of a Type II error (given a false null hypothesis) decreases, but the maximum probability of a Type I error (given a true null hypothesis) remains alpha by definition.
What do you mean by statistical error?
A statistical error is the (unknown) difference between the retained value and the true value. Context: It is immediately associated with accuracy since accuracy is used to mean “the inverse of the total error, including bias and variance” (Kish, Survey Sampling, 1965). The larger the error, the lower the accuracy.
Why are variances always the same when adding or subtracting?
So whether we’re adding or subtracting the random variables, the resulting range (one measure of variability) is exactly the same. That’s a plausibility argument that the standard deviations of the sum, and the difference should be the same, too.
What does it mean to do multiple comparisons in statistics?
“In Statistics, multiple testing refers to the potential increase in Type I error that occurs when statistical tests are used repeatedly, for example while doing multiple comparisons to test null hypotheses stating that the averages of several disjoint populations are equal to each other (homogeneous).”
Why are variances in statistics important to students?
The name helps students remember both the relationship and the restriction. As you may suspect, this analogy is more than a mere coincidence. There’s a nice geometric model that represents random variables as vectors whose lengths correspond to their standard deviations.
How to deal with the multiple hypothesis problem?
The more contrast between the variations, the more chance for different users behavior and significant results. One of the more efficient solutions to deal with the multiple test problem is to create variations that are significantly different from each other and therefore can provide a significant test result.