Video: Academic editor Denise Mager talks about how she decides what statistical test to conduct

Statistics play a crucial role in informing decision-making, making predictions, and drawing meaningful conclusions. However, before delving into the data analysis, it is essential to clarify the research question or objective that you aim to address. This foundational step is crucial as the techniques employed for data analysis will directly influence the conclusions drawn. 

To begin, it is important to ensure that you are comparing "apples with apples." This entails assessing whether your data satisfies certain assumptions and considering the types of variables involved. If the common assumptions, such as independence of observations, homogeneity of variance, and normality, are not met, it may be necessary to employ nonparametric statistical tests. Additionally, it is important to identify whether the variables in your study are categorical or quantitative in nature. 

Fortunately, there are various tools available to assist in selecting the most appropriate statistical test that will enable you to derive stronger inferences from the data. These tools consider the characteristics of your data and the specific objectives of your study, aiding in the selection of the most suitable test. 

By carefully considering the research question, assessing data assumptions, and understanding the nature of variables, you can make informed decisions regarding the statistical tests to be employed. This thoughtful approach enhances the validity and reliability of your analyses, ultimately leading to more robust conclusions. 

Academic editor Denise Mager talks about how she decides what statistical test to conduct. Watch her video here.

Would you like a 1:1 consultation with an expert statistician? Check out Editage’s Statistical Analysis & Review Service.