The following paper discussed Table 1 in observational studies, which talked about the use of p-values. Hope it helps others interested in this.
Who is in this study, anyway? Guidelines for a useful Table 1
The appropriateness of including a column containing inferential statistics (e.g. p-values) is a topic of some controversy. Statistical testing of distributions of variables (e.g. between exposed and unexposed) is common and even occasionally required by journals;1,6,9,10 although this is a tempting way to assess confounding, it is not best practice. Statistical significance is often misunderstood: non-significance of a p-value does not indicate that no difference in the distribution of a variable exists, and significance does not mean that the difference is meaningful or that the difference indicates presence of confounding.10–13 As a result, confounder assessment should not be based on p-values (Figure 2, Point 3; Figure 3, Point 4).1,2 Rather, authors should consider whether the relationship between the exposure and hypothesized confounders is as expected according to the causal theory, and consider whether the magnitude of an observed difference for a potential confounder represents a meaningful difference.1,9,10 Similarly, when considering external validity, statistical tests are not a helpful way to assess meaningful differences between source and target populations.