Hypothesis Testing and Regression in Public Health

Hypothesis Testing and Regression in Public Health

In public health, one can use various statistical methods to show relationships among variables. Such tests include but not limited to hypothesis testing and regression analysis. The two are distinct and serve a significant role in the public health sector, whose underestimation is unthinkable. In essence, showing their distinctness and application are at the heart of this discussion.

First and most importantly, is the difference between hypothesis testing and regression analysis. In this method, the main aim is to prove the validity of assertion (hypothesis) made at the beginning of a study. In other words, hypothesis testing helps one to infer causation between study variables. On the other hand, regression analysis attempts to find out the correlation between variables rather than infer causation (Holmes, 2009). Clearly, it is evident that the two are distinct from their purposes.

Secondly, identifying instances of their use in the public health sector will further illustrate their differences and forge new understanding about their vitality. Firstly, are the indications for using hypothesis testing. It is applicable in all studies that aim to establish the cause and effect. An example of such is in the proving of the role cholesterol plays in causing a high blood pressure (Hedges & Williams, 2014). Regression is not applicable in a case of this kind since the aim is to establish causation and not the relationship between the two variables.

Lastly, use of regression is inevitable in studies that involve sexual and reproductive health issues. Such is the case given the infinite number of research that utilize this statistical method. An example in point is the prediction of factors that were more likely to cause a particular sexual behavior among different persons of different gender and age groups (Constantine, 2012). In such a case, inferring causation is insensible while showing a relationship between the variables (regression) makes absolute sense.

In closure, from the illustrations, it is without a doubt that hypothesis testing and regression analysis are essential in public health. However, they are distinct approaches whose indications for use are a prerequisite for their proper application. Thus, medical personnel must understand the same if they are to use this tool effectively.

















Constantine, N. (2012). Regression Analysis and Causal Inference: Cause for Concern?. Perspectives On Sexual And Reproductive Health, 44(2), 134-137. http://dx.doi.org/10.1363/4413412

Hedges, C. & Williams, B. (2014). Anatomy of research for nurses.

Holmes, L. (2009). Basics of public health core competencies. Sudbury, Mass.: Jones and Bartlett.


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