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Factors Contributing To Sample Size Collection
Sample Size – Affecting Factors: There are a number of factors that contribute to the size of a sample. The most important factors are the population size and the desired level of accuracy. Other factors that can influence the sample size include the precision of the estimate, the confidence level desired, the variability of the population, and the cost of sampling.
In statistics, the sample size is the measure of the number of individual samples used in an experiment. For example, if we are testing 50 samples of people who watch TV in a city, then the sample size is 50. We can also term it sample statistics
Statistics is the study of the process of collecting, organizing, analyzing, summarizing data and drawing inferences from the data so worked on. In Statistics, we come across two types of data –
- Population data
- Sample data
Population data is a large amount of data that includes the whole area of study, which is termed as population. A population consists of all the elements that are studied for the research.
On the other hand, sample data is a part of the population. Usually, it is quite clumsy and difficult to compute the whole population. In this case, a representative sample selected from the population. This sample is termed sample data. In this article, let us discuss the sample size definition, formulas, examples in detail.
Sample Size Definition
A sample size is the number of items or people in a sample. The size of a sample can be determined in two ways: by counting the number of items in the sample or by estimating the population size and using a sampling fraction. A sample size of 30 is generally considered to be large enough for most purposes, but a sample size of 10 may be adequate for some purposes.
What Dangers Are Related to Small Sample Size?
The dangers associated with small sample size are many and varied. First and foremost, a small sample size can lead to inaccurate results. This is because a small sample size is not representative of the population as a whole, and therefore the findings of a study with a small sample size may not be accurate. Additionally, a small sample size can lead to bias in the results of a study. This is because a small sample size can more easily influenced by chance or by selection bias. Additionally, a small sample size can lead to a lack of statistical power, which can lead to inaccurate conclusions being drawn from a study. Finally, a small sample size can also lead to type II errors, which occur when a study finds no difference between the groups being studied when in fact a difference exists.
Calculating the Sample Size
The sample size is important for two reasons:
- It determines the accuracy of the poll results. The larger the sample size, the more accurate the poll results will be.
- It determines the cost of the poll. The larger the sample size, the more expensive the poll will be.
- There is no one definitive answer to the question of how large the sample size should be. It depends on the purpose of the poll and the level of accuracy desired.
- A good rule of thumb is to use a sample size that is large enough to produce results that are within 3% of the actual population value. This level of accuracy generally considered to acceptable for most purposes.
