Types/analyzing

Sample variability

the value of statistics vary in repeated random sampling.

Sampling distribution

A probability distribution of all possible sample means or proportions for a given sample size, showing the extent to which statistics vary due to sampling.

Sample distribution

A distribution that shows the frequencies of different outcomes in a sample, reflecting how individual observations are spread.

Population distribution

The distribution of all possible values or characteristics in a population, showing how these values are spread across the entire population.

Variability of a statistic

refers to how much a statistic is expected to fluctuate from sample to sample. It indicates the degree of dispersion in the values of the statistic due to random sampling.

10% condition

A guideline stating that a sample size should be at most 10% of the population size to ensure representativeness.

Normal condition

np >= 10 AND (1-p) >= 10, for a normal distribution

Unbiased estimator

An estimator that provides the true parameter value on average, meaning its expected value equals the parameter it estimates.

Biased estimator

An estimator that systematically overestimates or underestimates the parameter it is intended to estimate, leading to inaccurate conclusions.

Central limit theorum

States that the distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the original distribution of the population, provided the samples are independent and identically distributed.

Ideal number of samples

More than or equal to 30 samples for a normal distribution.