Conceptual validity refers to how well specific research maps onto the broader theory that it was designed to test. If a researcher has done a good job of converting the abstract to the observable, construct validity is high. Confidence intervals indicate how likely it is that the population parameter falls within the range, and how wide or narrow the range is. Construct validity also reflects the quality of ones operational definitions. For example, a 95% confidence interval for the population mean is the sample mean plus or minus 1.96 times the standard error. Confidence intervals are calculated by using the sample statistic, the standard error, and the confidence level. Confidence intervals are ranges of values that contain the population parameter with a certain level of confidence, such as 95% or 99%. Sampling error occurs because of the natural variation and randomness in the sampling process, and it can be estimated by using the standard error of the sample statistic. Sampling error is the difference between the sample statistic and the population parameter, such as the difference between the sample mean and the population mean. How do you know how accurate and precise your sample data is, and how much it differs from the population data? One way to measure this is by using sampling error and confidence intervals.
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