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In this case, two-sample t-test should be applied to compare the mean values of two samples.
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Two samples could be considered independent if the selection of the individuals or objects that make up one sample does not influence the selection of the individuals or subjects in the other sample in any way. In some cases, the independence can be easily identified from the data generating procedure. If not, what’s the reason for correlation? According to Kirkwood: ‘When comparing two populations, it is important to pay attention to whether the data sample from the populations are two independent samples or are, in fact, one sample of related pairs (paired samples)’. The reason for this confusion revolves around whether we should regard two samples as independent (marginally) or not. Although this fact is well documented in statistical literature, confusion exists with regard to the use of these two test methods, resulting in their inappropriate use. If the data is normally distributed, the two-sample t-test (for two independent groups) and the paired t-test (for matched samples) are probably the most widely used methods in statistics for the comparison of differences between two samples. If the outcome data are continuous variables (such as blood pressure), the researchers may want to know whether there is a significant difference in the mean values between the two groups. The statistical methods used in the data analysis depend on the type of outcome. In clinical research, we usually compare the results of two treatment groups (experimental and control).