Remembering that all research has some error, respond to at least one colleague’s post and comment on how we as social change agents and critical consumers of research can balance the usefulness with the error in the research. Do we throw the research out because of too much error, or is there something useful that it can tell us?



       The study "Complexity of Prostate Cancer Diagnosis in African American Men in the United States" focuses on understanding the challenges and intricacies surrounding the diagnosis of prostate cancer in African American men within the United States. The research aims to shed light on the factors contributing to the complexity of diagnosis in this population.

       Dietz and Kalof (2009) noted the Y=f(X) + E notation, the dependent variable (Y) would be the complexity of the prostate cancer diagnosis. The independent variables (X) could include factors such as race or ethnicity, socioeconomic status, access to healthcare, awareness of prostate cancer screening, cultural beliefs, and healthcare system factors (p. 33).

       The research is helpful to the population of African American men (AAM) in the United States who are at risk of prostate cancer. Social change can occur if it is learned that prostate cancer awareness (PSA) and education are generating the preferred impact that would reduce the incidence of prostate cancer in AAM. By examining the complexity of diagnosis, the study may contribute to improved understanding and awareness among healthcare providers, policymakers, and the community. It has the potential to identify barriers and inform strategies to enhance early detection and improve health outcomes for AAM-facing prostate cancer (Smith, 2016).

        Regarding George Box's (1919–2013) quote, "All models are wrong, some models are useful," it implies that models or theories are simplified representations of reality. While they may not capture every nuance, they can still provide valuable insights and practical applications. In the context of the research models presented in the study, they may be "wrong" because they may not encompass all the complexities and individual variations present in the diagnosis of prostate cancer in AAM. However, they can still help identify patterns, highlight significant factors, and guide interventions.

       The potential errors in the reported research, various errors could be present. These could include sampling errors, measurement errors, confounding variables not adequately controlled for, data collection biases, or limitations in the statistical methods employed. Researchers need to acknowledge and address these potential errors to ensure the accuracy and reliability of their findings.


Dietz, T., & Kalof, L. (2009). Introduction to social statistics: The logic of statistical reasoning. West Sussex, United Kingdom: Wiley-Blackwell. Introduction to Social Statistics: The Logic of Statistical Reasoning, 1st Edition by Dietz, T.; Kalof, L. Copyright 2009 by John Wiley & Sons – Books. Reprinted by permission of John Wiley & Sons – Books via the Copyright Clearance Center. Chapter 2, “Some Basic Concepts” (pp. 33–63)

George E. P. Box (1919–2013)

Sumlin, A. B. (2016). Complexity of Prostate Cancer Diagnosis in African American Men in the United States.  Walden Dissertations and Doctoral Studies