EDA212

should be carefully fixed so that it will be adequate to draw valid and generalized conclusions.

Also known as sampling error

The range in which the true value of the population is estimated to be.

The key idea in the Central Limit Theorem is that when a population is repeatedly sampled, the average value of the attribute obtained by those samples is equal to the true population value. In a normal distribution, approximately 95% of the sample values are within two standard deviations of the true population value. In other words, this means that if a 95% confidence level is selected, 95 out of 100 samples will have the true population value within the range of precision specified.

a range of values we are fairly sure our true value lies in.

Refers to the distribution of attributes in the population. The variables with more homogeneous population, the smaller the sample size required. If the more heterogeneous population, the larger the sample size required to obtain a given level of precision.

Eliminates sampling error. Use the entire population as the sample. Although cost considerations make this impossible for large populations, a census is more attractive for small populations.

Without reviewing the methods used in these studies may run the risk of repeating errors that were made in determining the sample size for another study.

A third way to determine sample size is to rely on published tables, which provide the sample size for a given set of criteria.

Cochran Formula, Taro Yamane Sampling Formula

allows you to calculate an ideal sample size given a desired level of precision, desired confidence level, and the estimated proportion of the attribute present in the population. Considered especially appropriate in situations with large populations.

𝒏𝒐 = 𝒛 ^2pq/e^2