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23 lip 2018 · In inferential statistics, we use sample statistics to estimate population parameters. For example, if we collect a random sample of adult women in the United States and measure their heights, we can calculate the sample mean and use it as an unbiased estimate of the population mean.
27 lis 2020 · A parameter is a number describing a whole population (e.g., population mean), while a statistic is a number describing a sample (e.g., sample mean). The goal of quantitative research is to understand characteristics of populations by finding parameters.
14 maj 2019 · A parameter is data that describes the entire population, while a statistic is data that describes a sample of that population. A sample is a part, or a subset, of a population. With a well-designed study, a sample statistic may provide an accurate estimate of a population parameter.
In statistics, as opposed to its general use in mathematics, a parameter is any quantity of a statistical population that summarizes or describes an aspect of the population, such as a mean or a standard deviation.
8 sty 2024 · knitr::kable(data.frame(stringsAsFactors=FALSE, Symbol = c("$\\bar{X}$", "$\\mu$", "$\\hat{\\mu}$"), What.is.it = c("Sample mean", "True population mean", "Estimate of the population mean"), Do.we.know.what.it.is = c("Yes calculated from the raw data", "Almost never known for sure", "Yes identical to the sample mean")))
If we had census data from a population available to us, we could calculate parameters, or corresponding summary measures of characteristics from the population. The parameter is the true but often unknown value that we would ideally like to know. Since populations are generally fixed, a parameter is generally also a fixed number.
During sampling, individual elements (usually people) are randomly drawn from the population and we can calculate sample statistics from the resulting sample. Parameter estimation uses these statistics to make inferences about the unknown population parameters.
