Logistic regression is a statistical method used to predict a binary outcome (yes or no, true or false). It calculates the odds ratio, which is the probability of the outcome occurring. This method does not require the same assumptions as linear regression. It can be used in research studies where the outcome is categorical rather than numerical, and can consider multiple variables and adjust for confounding factors. A study on prescribing habits among podiatrists is used as an example of the application of logistic regression. Cohort studies are a type of longitudinal, observational study that follow participants over time to assess the relationship between an exposure and an outcome. They are similar to randomized control trials in that they both track outcomes over time, but they differ in that cohort studies are observational and do not randomly assign participants to an exposure group. Retrospective cohort studies, also known as chart review studies, are used to examine associations between exposures and outcomes in cases where randomized control trials are not possible or ethical.

Statistical tests such as the Chi-Square test and Fisher's Exact test are commonly used in research to determine if there is a significant association or difference between two variables. They can be used to compare groups, survey populations, assess response bias, and more. Negative studies, where the p-value is not significant, are also important and should still be published. A case series is a detailed report of a group of subjects who are all experiencing the same outcome. It is typically used for rare or novel diseases and can offer insight into potential links between outcomes and exposures or interventions. Some limitations of case series include the lack of a control group and the difficulty in establishing causal relationships. However, they are inexpensive and can lead to the discovery of new diseases and treatments. Two statistical tests, Fisher's exact test and Pearson's chi squared, are often used in case studies.

The episode discusses the history of statistics and its importance in research, especially in the medical field. It also explains the different types of statistics (frequentist and Bayesian) and the importance of being able to critically appraise research papers and choose the appropriate research design and statistical analysis. The episode ends by encouraging researchers to tune in for future episodes that will cover different research designs and statistical analyses in depth. This is week one and the introductory episode. Our Hosts share their academic and professional background along with our excitement about presenting Pediatric research info that podiatrists can use to become better academics. We go on to explore the purpose, strengths, weaknesses and uses of a Case Report study design. We conclude with a brief historical discussion and relay that we plan to bring a new research design every week based on the frequentist approach to statistics.