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Selecting the optimal sample size is crucial for quantitative researchers seeking precise and accurate results from significance tests. Determining the sample size involves various methods, such as statistical formulas and electronic sample size calculators. However, researchers must first establish details about the target population to identify the appropriate sample size. They must consider important factors such as the population size, error level, confidence interval, and confidence level. These determinants aid in deciding how much the sample mean can deviate from the population mean and how sure researchers want to be that the actual mean will fall within their confidence range. The confidence interval is usually set at 90%, 95%, or 99% confidence.
Furthermore, researchers must also account for the standard deviation to anticipate the variation between responses. The size of the quantitative sample is assessed based on the strength of a hypothesis test and the quality of produced estimates (Mwansa et al., 2022). Five important study design parameters, such as the significance criterion, minimum expected difference, estimated measurement variability, desired statistical power, and one or two-tailed statistical analysis, typically determine the primary determinant of sample size in a quantitative design.
