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Available literature underscores the need for researchers to develop a comprehensive understanding of statistical terms and methodologies if they are expected to develop evidence-based public health policies, achieve optimal social and health outcomes, as well as minimize health inequalities affecting populations in contemporary contexts (Dancey, Reidy, & Rowe, 2012). Although there are many statistical terms, it is evident that some terms are more important than others based on the context of use and applicability in real-life situations. The present paper aims to further this proposition by demonstrating why the one-sample t-test and estimation for the Poisson distribution are important in public health contexts.
The one-sample t-test has been selected due to its usefulness in assisting researchers to determine whether a sample that has been selected to conduct a study comes from a population with a specific mean by examining the mean variation between the sample and the known or hypothesized value of the population mean (Boslaugh, 2013). Having this information is of immense importance to public health researchers as it helps them to understand what to expect if the sample comes from a particular population and what to do if calculations conclude that the sample was sourced from a different population. In practical contexts, the findings of a research study aimed at evaluating the causes of dental caries in a particular community may not be generalized to the population if one sample t-test calculations demonstrate significant variations between the mean of the sample and the population mean. As such, this test is very important in helping researchers to avoid making mistakes that could affect the generalizability of their study findings to the population. Additionally, the one-sample t-test helps researchers to understand important characteristics of the sample by allowing them to make a statistical evaluation about the level or degree of divergence between the sample mean and the national average (Boslaugh, 2013). This is important in public health research as it helps in isolating important characteristics that are to be studied from a population characterized by a multiplicity of characteristics.
The estimation for the Poisson distribution has been selected due to its usefulness in assisting public health researchers to estimate the likelihood of a set of independent events or experiences taking place in a predetermined time frame and/or space (Rosner, 2015). This is important in epidemiological research as it helps researchers to not only determine the probability of individuals developing a particular disease but also to examine cases in which a particular health intervention is ineffective in managing the disease. Because epidemiological research is interested in investigating disease incidence, origins, and effects in distinct communities, public health researchers need to develop an adequate understanding of the Poisson distribution to be able to determine the probability of a given number of events (disease incidence, origins, and effects) occurring in a fixed interval of time and/or space. Researchers with such an understanding are better able to develop effective interventions and evidence-based public health policies with the capacity to address a multiplicity of issues in disease prevalence and causes.
This paper has explained why the one-sample t-test and estimation for the Poisson distribution were selected for discussion. Overall, the paper has been successful in demonstrating that the two statistical terms have useful applications in public health and epidemiology domains, thus their selection.
References
Boslaugh, S. (2013). Statistics in a nutshell (2nd ed.). Sebastopol, CA: OReilly Media, Inc.
Dancey, C., Reidy, J., & Rowe, R. (2012). Statistics for the health sciences: A non-mathematical introduction. Thousand Oaks, CA: Sage Publications Ltd.
Rosner, B. (2015). Fundamentals of biostatistics (8th ed.). Boston, MA: Cengage Learning.
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