![]() Confidence intervals indicate how likely it is that the population parameter falls within the range, and how wide or narrow the range is. For example, a 95% confidence interval for the population mean is the sample mean plus or minus 1.96 times the standard error. ![]() ![]() Therefore, reliability, validity and triangulation, if they are relevant research concepts, particularly from a qualitative point of view, have to be redefined in order to reflect the multiple ways of establishing truth. The validity test is carried out to measure the extent to which the concept can be measured accurately in a quantitative. Confidence intervals are calculated by using the sample statistic, the standard error, and the confidence level. the validity and reliability of a qualitative study. The validity and reliability tests were carried out using IBM SPSS25. 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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