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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.
An explanation of the difference between a statistic and a parameter, along with several examples and practice problems.
Parameters are numbers that describe the properties of entire populations. Statistics are numbers that describe the properties of samples. For example, the average income for the United States is a population parameter. Conversely, the average income for a sample drawn from the U.S. is a sample statistic.
Parameters are fixed numerical values for populations, while statistics estimate parameters using sample data. Both are key in data analysis, with parameters as true values and statistics derived for population inferences.
In the statistical and data analytics areas, parameters and statistics are the most widely used two terms. Statistic is a numerical value calculated from a sample of data, whereas, Parameter is a numerical value that describes a characteristic of an entire population.
The difference between a parameter vs a statistic is that a parameter is a fixed measure describing the whole population, while a statistic is a characteristic of a sample, a portion of the target population.
The most important difference between statistic and parameter is that, parameter is a numerical value that describes entire population whereas statistic is a measure which describe a small subset of population.
The difference between a statistic and a parameter is that statistics describe a sample. A parameter describes an entire population. For example, you randomly poll voters in an election. You find that 55% of the population plans to vote for candidate A. That is a statistic. Why?
Statistics describe sample data, while parameters describe entire populations. Statistics are subject to variability due to sampling, whereas parameters are fixed values. Sample mean and sample standard deviation are typical examples of statistics. Population mean and population standard deviation are examples of parameters.
There is a simple and straightforward way to remember what a parameter and statistic are measuring. All that we must do is look at the first letter of each word. A parameter measures something in a population, and a statistic measures something in a sample. Below are some more example of parameters and statistics: