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Why is having a larger sample size be a better idea than having a small sample size when doing a experiment?
1. Better chance of uniform sample. 2. Material for confirmations if needed.
Does the size of a sample affect the values of the frequency table?
Yes. If the sample is a random drawing from the population, then as the size increases, the relative frequency of each interval from the sample should be a better estimate of the relative frequency in the population. Now, in practical terms, increasing a small sample will have a larger effect than increasing a large sample. For example, increasing a sample from 10 to 100 will have a larger effect than increasing a sample from 1000 to 10,000. The one exception to this, that I can think of, is if the focus of the study is on a very rare occurrence.
What is domain sampling in statistics?
It's a model for measuring reliability of measures of a construct. First you choose randomly a finite number of items from an infinite pool of items to measure the construct and then use it as a criterion to evaluate reliability of other chosen samples. The higher the correlation of the scores derived using any random sample with the score derived using the criterion sample, the higher the reliability of the random sample
Why does a statistical sample have to be large?
The larger the sample of data collected leads to a more accurate conclusion.
How does sample size affect the experiment?
The sample size determines the accuracy of results in an experiment
