When undertaking quantitative hypothesis testing, social researchers need to decide whether the data with which they are working is suitable for parametric analyses to be used. When considering the relevant assumptions they can examine graphs and summary statistics but the decision making process is subjective and must also take into account the robustness of the proposed tests to deviations from the assumptions. We review the contemporary advice on this issue available to researchers and look back to the roots of hypothesis testing and associated work undertaken by eminent statisticians since the 1930s. From this we create a set of flow charts to give researchers tools they can use to make decisions in a more objective manner.