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A population is a group of phenomena that have something in common. The term often refers to a group of people, as in the following examples:
All registered voters in Crawford County
All members of the International Machinists Union
All Americans who played golf at least once in the past year
But populations can refer to things as well as people:
All widgets produced last Tuesday by the Acme Widget Company
All daily maximum temperatures in July for major U.S. cities
All basal ganglia cells from a particular rhesus monkey
Often, researchers want to know things about populations but don't have data for every person or thing in the population. If a company's customer service division wanted to learn whether its customers were satisfied, it would not be practical (or perhaps even possible) to contact every individual who purchased a product. Instead, the company might select a sample of the population. A sample is a smaller group of members of a population selected to represent the population. In order to use statistics to learn things about the population, the sample must be random. A random sample is one in which every member of a population has an equal chance to be selected.
A parameter is a characteristic of a population. A statistic is a characteristic of a sample. Inferential statistics enables you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population.
簡單黎講, population 係大數目(eg. 大於100 個數據) 既統計數據, 由population 反應既數據通常都under normal distribution.
sample 係細數目既統計數據 (eg. 小於10個data). 反應既數據唔會係一個靚既normal distribution curve. 如果用normal distribution 去estimate sample 既data, 就唔會太準~ 所以sample statistics 會有另外既formula.