# Population Parameters

Ideas and Concepts of Estimating Population Parameters

Population parameters and sample statistics are different even though they appear similar. A population parameter is a description of an entire population, while sample statistics describe a sample extracted from a given defined population. With population parameters, it is often difficult to obtain descriptive figures unlike in the sample statistics. Each population parameter has its own corresponding statistic that is measurable. As enumerated by Osborn (2018), a population parameter is a fixed number, while a sample statistic is dependent on a sample size.

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## How to Estimate Population Parameters

The Ordinary Least Squares (OLS) is a proven method of estimating population parameters. It is based on the premise of averages. As such, the OLS regression line is located to minimize the sum of squared distances between individual points and the regression line. Another alternative method is the use of the maximum likelihood estimation. The method works by determining the parameters that maximize the likelihood from a given observation

## Confidence Interval

It is a range of values defined such that there is a specified probability that the value of the parameter lies within it (Stat Trek, 2018). It is an indication of the frequency of potential confidence intervals that might exist within an unknown population parameter. As such, it comprises a range of possible values that are representative of a given unknown population parameter.

## Margin of Error

It is a representation of a range of values that exist below and above the sample statistic in a confidence interval. It is an indication of how the results acquired, in percentage points, will be different from the real population value and it is given by the formula, E= zα/2 (ϭ/√ n). In simple terms, the Margin of Error for a sample statistic is a product of the critical value and the standard deviation of the statistic in question.

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