Making Statistics more Meaningful

The opening argument of Henry May for making interpretations of results of different statistical methods and models understandable to all stakeholders is quite convincing. No matter how fancy or complex models you use in your analysis, it bears a little value if it fails to communicate your explanation of reality to not only other researchers of your troop, but to policy makers and other common stakeholders in your community. If you explain something that exists in reality, it deserves to be understood by almost everyone whether it is Einstein’s theory of relativity or Hawking’s theory of big bang or Darwin’s theory of evaluation or Marx’s theory of dialectical materialism. We all should bear it in mind that statistics is just a simple tool for analysing all information in our hand to explain the nature of target research problems where statistics is dedicated to research, not research is for statistics. When we start our journey of research with formulating research questions, statistics doesn’t join us at the beginning rather the time we decide what type of data we will use to explain our research questions, we invite statistics as our aide to help us organise, summarise or explain the dynamics of relations among variables if you choose to collect quantitative data for our study.  Like the beginning, when we obtain the results of statistical methods or models, we need to explain or interpret them in plain English or any other languages as we have already formulated our research problems.

May, in his article, suggested guidelines for meaningful presentation of statistical outputs. He indicated three major features of meaningful presentation, which includes understandability, interpretability, and comparability. By understandability May meant “The results should be reported in a form that is easily understood by most people by making minimal assumptions about the statistical knowledge of the audience and avoiding statistical jargon” (May, 2004, p.527). He simply suggested to reduce or eliminate statistical jargons as much as possible, and to explain them in plain language. By interpretability, May simply meant the familiarity of the metrics or units of measure, which the statistic is based upon.  The more the unit of expressions is familiar to the audience, the more the interpretability is. The third feature, May emphasised is the comparability which refers to the characteristics of statistic of being compared across different factors of a single study or to the effects from other studies.

To make his argument more understandable to us, May came up with some examples from our everyday practices. He categorised statistical analysis into two major groups: descriptive statistics and relational statistics against the traditional categorization of statistics as descriptive and inferential nature. As we know, descriptive statistics usually describe some general aspects of a distribution such as percents, proportions, average, ratios, and sometimes variance and skewness in order to gauge the severity of problems. Most audience are familiar with percents, proportions and average, and may be a little familiar with standard deviation or variance and skewness. May suggested phrasing variance and skewness in the context of distributional density. I would simply say that standard deviation or variance tells us how good a distribution is in terms of Whether the data are distributed close around the average or  far around. Standard deviation doesn’t tell us about the direction of data. Skewness can tell us this story whether most data are scattered below the average or above it along with the density or compactness of them.

According to May, relational statistics are used to describe and gauge the strength of relationships between two or more variables, or to estimate the effect of one variable on another. Most commonly used relational statistics are linear modeling that includes analysis of variance (ANOVA), correlation, regression analysis, path models, and hierarchical linear models (HLM). To make correlations more meaningful, May suggested to explain the possible values for correlations (i.e. -1 to 1), and to explain other characteristics of correlations. Correlational results can also be reported using visual effects such as scatter plot or diagrams, which will help audience better understand the relations. To make regression coefficients more meaningful, he suggested explaining how the expected change in an outcome comes for per unit change in a predictor, and how the coefficient of determination R√ indicates the variation in the outcome variables explained by the predictor.

I personally believe that considering the nature of problems, we should use statistical tools as simple as possible. Why I should bother for a computer if I can solve a problem with a calculator.  If I am bound to use a computer I need to explain the outputs in a general language understandable to all since this clever machine has a language of its own, which is not spoken by all. Thus, the user-friendly interpretations of statistical results will help communicate the nature and dynamics of the problems to the researchers, policy makers and all possible stakeholders, and will benefit the community at large.


-May, H. 2004. Making statistics more meaningful for policy research and program evaluation. American Journal of Evaluation. 25. p. 525-540.  

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