Q: What are the disadvantages of having a small sample size?

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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.

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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 property that depends on the size of the sample is extensive. Extensive properties, such as mass and energy, scale with the size of the sample. This means that as the sample size increases, the value of the property also increases proportionally.

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.

Poor eyesight, small size, dependent on man

A small sample size and a large sample variance.

having a large sample size