# 10: Estimation

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One of the major applications of statistics is estimating population parameters from sample statistics. For example, a poll may seek to estimate the proportion of adult residents of a city that support a proposition to build a new sports stadium. Out of a random sample of 200 people, 106 say they support the proposition. Thus in the sample, 0.53 of the people supported the proposition. This value of 0.53 is called a point estimate of the population proportion. It is called a point estimate because the estimate consists of a single value or point

• 10.1: Introduction to Estimation
One of the major applications of statistics is estimating population parameters from sample statistics. For example, a poll may seek to estimate the proportion of adult residents of a city that support a proposition to build a new sports stadium. Out of a random sample of 200 people, 106 say they support the proposition. Thus in the sample, 0.53 of the people supported the proposition. This value of 0.53 is called a point estimate of the population proportion. This is called a point estimate.
• 10.2: Degrees of Freedom
Some estimates are based on more information than others. For example, an estimate of the variance based on a sample size of 100 is based on more information than an estimate of the variance based on a sample size of 5. The degrees of freedom (df) of an estimate is the number of independent pieces of information on which the estimate is based.
• 10.3: Characteristics of Estimators
This section discusses two important characteristics of statistics used as point estimates of parameters: bias and sampling variability. Bias refers to whether an estimator tends to either over or underestimate the parameter. Sampling variability refers to how much the estimate varies from sample to sample.
• 10.4: Bias and Variability Simulation
This simulation lets you explore various aspects of sampling distributions. When it begins, a histogram of a normal distribution is displayed at the topic of the screen.
• 10.5: Confidence Intervals
• 10.6: Confidence Intervals Intro
Confidence intervals provide more information than point estimates. Confidence intervals for means are intervals constructed using a procedure that will contain the population mean a specified proportion of the time, typically either 95% or 99% of the time. These intervals are referred to as 95% and 99% confidence intervals respectively.
• 10.7: Confidence Interval for Mean
When you compute a confidence interval on the mean, you compute the mean of a sample in order to estimate the mean of the population. Clearly, if you already knew the population mean, there would be no need for a confidence interval. However, to explain how confidence intervals are constructed, we are going to work backwards and begin by assuming characteristics of the population. Then we will show how sample data can be used to construct a confidence interval.
• 10.8: t Distribution
The tt distribution is very similar to the normal distribution when the estimate of variance is based on many degrees of freedom, but has relatively more scores in its tails when there are fewer degrees of freedom.  The tt distribution is therefore leptokurtic. The t distribution approaches the normal distribution as the degrees of freedom increase.
• 10.9: Confidence Interval Simulation
• 10.10: Difference between Means
It is much more common for a researcher to be interested in the difference between means than in the specific values of the means themselves.
• 10.11: Correlation
• 10.12: Proportion
• 10.13: Statistical Literacy
• 10.E: Estimation (Exercises)

This page titled 10: Estimation is shared under a Public Domain license and was authored, remixed, and/or curated by David Lane via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.