Last, we review and demonstrate common pitfalls with the presentation of data in graphs, which adds another potential opportunity to introduce bias. Conclusions The tests used in the medical literature continue to change and evolve, usually for the better. With these changes, there will certainly be opportunities to introduce unintentional bias. The more aware we are of this, the more likely we are to find it and correct it.Background Medical knowledge is constantly growing at an exponential rate. Despite this growth, it is estimated to take 17 years for medical innovation to reach the bedside and improve clinical care. Implementation science is the scientific study of methods to facilitate the update of evidence-based practice and research into regular use and policy. Discussion Implementation science offers theories, models, and frameworks aimed at decreasing the time it takes to get medical innovation to the patient and to sustain the care improvements. Implementation science principles center around five main fundamental concepts that include information diffusion, dissemination, implementation, adoption, and sustainability. Understanding these fundamental concepts allow clinicians to prepare for an implementation by asking the correct questions such as Are we ready for change?; What is our current process that we want to change?; Who needs to be involved in the implementation?; and How do we measure success? This article describes a successful catheter-associated urinary tract infection quality improvement program implemented using implementation science principles. Conclusion Implementation science offers many proven tools and strategies to implement new evidence-based medicine and medical innovations into common practice. Clinicians are often the leaders of change and should develop an understanding of implementation science fundamentals to allow successful implementation of quality improvement and research initiatives.Background The goal of a randomized or observational study is to develop an unbiased and reliable answer to a therapeutic question. However, there are multiple pitfalls in the reporting and interpretation of data that can compromise our ability to evaluate the pragmatism and the effectiveness of the intervention being studied. Researchers must be conscious of these biases when designing their studies, just as readers must be aware of these potential pitfalls when interpreting results. Results The purpose of this review is to highlight some of the more common sources of bias in clinical research, including internal and external validity, type 1 and type 2 error, reporting of secondary outcomes, the use of subgroup analyses, and multiple comparisons. This article also discusses potential solutions to these issues, including using the fragility index to understand the robustness of study conclusions, and generating an E value to determine the degree of unmeasured confounding in a study. Rimegepant solubility dmso Conclusions With an understanding of these pitfalls, readers can critically review scientific literature and ascertain the validity of the conclusions.Background Comparison of parameters between two or more groups forms the basis of hypothesis testing. Statistical tests (and statistical significance) are designed to report the likelihood the observed results are caused by chance alone, given that the null hypothesis is true. Methods To demonstrate the concepts described, we utilized the Nationwide Inpatient Sample for patients admitted for emergency general surgery (EGS) and those admitted with non-EGS diagnoses. Depending on the type and distribution of individual variables, appropriate statistical tests were applied. Results Comparison of numerical variables between two groups is begun with a simple correlation, depicted graphically in a scatterplot, and assessed statistically with either a Pearson or Spearman correlation coefficient. Normality of numerical variables is then assessed and in the case of normality, a t-test is applied when comparing two groups, and an analysis of variance (ANOVA) when comparing three or more groups. For data that are not distributed normally, a Wilcoxon rank sum (Mann-Whitney U) test may be used. For categorical variables, the χ2 test is used, unless cell counts are less than five, in which case the Fisher exact test is used. Importantly, both the ANOVA and χ2 test are used to assess for overall differences between two or more groups. Individual pair comparison tests, as well as adjusting for multiple comparisons must be used to identify differences between two specific groups when there are more than two groups. Conclusion A basic understanding of statistical significance, and the type and distribution of variables is necessary to select the appropriate statistical test to compare data. Failure to understand these concepts may result in spurious conclusions.Background The use of machine learning (ML) and artificial intelligence (AI) in medical research continues to grow as the amount and availability of clinical data expands. These techniques allow complex interpretation of data and capture non-linear relations not immediately apparent by classic statistical techniques. Methods This review of the ML/AI literature provides a brief overview for practicing surgeons and clinicians of the current and future roles these methods will have within surgical infection research. Results A conceptual overview of the techniques is provided along with concrete examples in the surgical infections literature. Further examples of ML/AI techniques in clinical decision support as well as therapy discovery with model-based deep reinforcement learning are illustrated. Conclusions Artificial intelligence and ML are important and increasingly utilized techniques within the expanding body of surgical infection research. This article provides a minimal baseline literacy in ML/AI to be able to view such projects in an appropriately critical fashion.Background The quality of scientific literature is judged by study design, validity, and applicability to unique patient populations. Methods We searched the available literature to explore the hierarchy of evidence, explain research fundamentals such as sample size calculation, and discuss common study designs employed in surgical research and the interpretation of trial designs. Results Each unique study design has restraints created by some degree of systematic errors and bias. This article provides definitions for the scientific boundaries of case control, retrospective, before-and-after, prospective observational, randomized controlled designs, and meta-analyses. Conclusion Critical thinking and appraisal of the literature is a skill that requires lifelong training and practice. Clinical research education and design need to garner more attention in the medical community.