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1. Better chance of uniform sample. 2. Material for confirmations if needed.
One is a small sample size, but that's just my answer, you might want to ask more people.
When the sample size is small
no
It is the number of elements in the sample. By contrast, the relative sample size is the absolute sample size divided by the population size.
1. Better chance of uniform sample. 2. Material for confirmations if needed.
less bias and error occur when sample size is larger
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.
One is a small sample size, but that's just my answer, you might want to ask more people.
Statistically the results will not be scientifically valid if the sample size is too small.
The volume and the mass of sample both depend on the size of the sample.A small sample has small volume and small mass, a big sample has big volumeand big mass. But the ratio of mass to volume is constant for a pure sample ofa substance, no matter what size the sample is. That ratio is called the densityof the substance.
When the sample size is small
no
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.
A small sample size and a large sample variance.
Poor eyesight, small size, dependent on man
having a large sample size