There are three main misconceptions of statistical significance:A significant result means that the effect is importantStatistical significance is not the same as practical significance.A non-significant result means that the null hypothesis is trueRejecting the alternative hypothesis does not mean we accept the null hypothesis.A significant result means that the null hypothesis is falseIf we reject the null hypothesis in favour of the alternative hypothesis, this does not mean that the null hypothesis is false, as rejection is all based on probability and there still is a probability of it not being false.The use of NHST encourages ‘all-or-nothing’ thinking. A result is either significant or not. If a confidence interval contains zero, it could be that the population effect might be zero. An empirical probability is the proportion of events that have the outcome in which you’re interested in an indefinitely large collective of events. The p-value is the probability of getting a test statistic at least as large as the one observed relative to all possible values of the null hypothesis from an infinite number of identical replications of the experiment. It is the frequency of the observed test statistic relative to all possible values that could be observed in the collective of identical experiments. The p-value is affected by the intention of the researcher as the p-values are relative to all possible values in identical experiments and sample size and time of collection of data (the intentions) could influence the p-values. In journals, based on NHST, there is a publication bias. Significant results are more likely to get published. Researcher degrees of freedom are ways in which the researcher could influence the p-value. This could be used to make it more likely to find a significant result (e.g. by excluding some cases to make the result significant). Researcher degrees of freedom could include not using some observations and not...