No.
Yes. Roughly, very large samples are very likely to have subsets data points having very similar means and distributions. Large numbers of such subsets will tend to be normal distributed (Why?) and will tend to make the total sample be normally distributed.
The sample mean is an estimator that will consistently have an approximately normal distribution, particularly due to the Central Limit Theorem. As the sample size increases, the distribution of the sample mean approaches a normal distribution regardless of the original population's distribution, provided the samples are independent and identically distributed. This characteristic makes the sample mean a robust estimator for large sample sizes.
If they are not matched pairs, it does not really matter. If the combined sample size is fixed (because of costs, say) then it is better to have a larger sample where more variability is expected.
First, I will give an example, similar to your question: -11000 -9000 +44000 mean = 8,000 and median = -9000. Symmetrical distributions after infinite sampling will show no difference in mean and median. Large differences are possible with small sample sizes even with symmetrical distributions. If the sample is large and the difference is large, this infers that the distribution is asymmetrical. The skewness of the distribution can be calculated.
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.
Accurate estimates of various statistics.
Yes, but it converges to the Gaussian (Normal) dirstribution for large sample sizes.
No.
Yes. Roughly, very large samples are very likely to have subsets data points having very similar means and distributions. Large numbers of such subsets will tend to be normal distributed (Why?) and will tend to make the total sample be normally distributed.
Sample sizes vary from designer to designer but a common range is between 0-2, 4 at most.
An allele ladder is used as a reference for determining the sizes of DNA fragments in a sample during DNA profiling. It contains known fragments of DNA of varying sizes that are used to calibrate the gel electrophoresis results, allowing for accurate comparison and identification of the sizes of DNA fragments in the sample.
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.
Equal variances, independent observations and normality
The number of trials and sample sizes generally increase the accuracy of the results because you can take the average or most common results in the experiment
Yes, storage tents come in large sizes. One can know this by assuming that storage has to be large in order to contain many objects. Therefore, storage tents do come in large sizes.
The variance decreases with a larger sample so that the sample mean is likely to be closer to the population mean.