The ideal sample size depends on several factors, including the population size, the desired confidence level, the margin of error, and the variability within the population. Generally, larger sample sizes yield more reliable results and reduce the margin of error. For most surveys, a sample size of 30 is often considered the minimum for general statistical analysis, but larger sizes (e.g., 100-400) are recommended for more accurate and generalizable findings. It's essential to conduct a power analysis to determine the specific sample size needed for your study's objectives.
They should be smaller for the sample size 80.
no
that you have a large variance in the population and/or your sample size is too small
Better the results
The size of the sample should not affect the critical value.
Yes, but that begs the question: how large should the sample size be?
A disadvantage to a large sample size can skew the numbers. It is better to have sample sizes that are appropriate based on the data.
They should be smaller for the sample size 80.
A sample size of one is sufficient to enable you to calculate a statistic.The sample size required for a "good" statistical estimate will depend on the variability of the characteristic being studied as well as the accuracy required in the result. A rare characteristic will require a large sample. A high degree of accuracy will also require a large sample.
The result will be closer to the truth.
The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.
It should reduce the sample error.
no
Statistically the larger the sample size the more significant the results of the experiment are. Chance variation is ruled out.
that you have a large variance in the population and/or your sample size is too small
Better the results
The Central Limit Theorem states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. This fact holds especially true for sample sizes over 30.