# Interval Estimation and Point Estimation

The problem with using a point estimate is that although it is the single best guess you can make about the value of a population parameter, it is also usually wrong. Interval estimate overcomes this problem using interval estimation technique which is based on point estimate and margin of error.

• A major advantage of using interval estimation is that you provide a range of values with a known probability of capturing the population parameter (e.g. if you obtain from SPSS a 95% confidence interval you can claim to have 95% confidence that it will include the true population parameter.
• An interval estimate (i.e., confidence intervals) also helps one to not be so confident that the population value is exactly equal to the single-point estimate. That is, it makes us more careful in how we interpret our data and helps keep us in proper perspective.
• Perhaps the best thing of all to do is to provide both the point estimate and the interval estimate. For example, our best estimate of the population mean is the value of $32,640 (the point estimate) and our 95% confidence interval is$30,913.71 to \$34,366.29.
• By the way, note that the bigger your sample size, the more narrow the confidence interval will be.
• If you want narrow (i.e., very precise) confidence intervals, then remember to include a lot of participants in your research study.

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