Statistics for Clinicians (On-Demand)-March16WebinarRecording

Video Transcription

Video Summary

In the video, Dr. Jennifer Wu discusses various statistical tests and regression analyses used for different types of data. She emphasizes the importance of understanding the type of data being analyzed (continuous, ordinal, or categorical) and the number of groups being compared. For example, a student's t-test is used for comparing continuous data between two groups, while a Mann-Whitney U test is used for comparing skewed or ordinal data between two groups. For more than two groups, a one-way ANOVA or Kruskal-Wallis test can be used, depending on the distribution of data. Dr. Wu also explains the concept of p-values and their interpretation. A p-value measures the probability that the observed difference between groups occurred by chance. A lower p-value indicates a higher likelihood that the difference is not due to chance, leading to the conclusion of statistical significance. The commonly used threshold for statistical significance is a p-value of 0.05 or less. In addition, Dr. Wu discusses regression analyses, including linear regression for continuous outcomes and logistic regression for categorical outcomes. She explains the interpretation of odds ratios and relative risks in logistic regression and cohort studies. Furthermore, she introduces survival analysis, including Kaplan-Meier survival curves and the log-rank test for comparing survival between groups. Overall, the video provides an overview of statistical tests and regression analyses used for different types of data, as well as an explanation of p-values and interpretation of results. No specific video credits were mentioned.<br /><br />The webinar covered various topics including different types of data, commonly used statistical tests, and how to choose the appropriate test for analyzing data. The webinar also discussed the significance of confidence intervals and how they indicate whether a result is statistically significant. Additionally, sample size calculations were discussed, highlighting the importance of considering factors such as alpha and power when determining sample size. Overall, the webinar aimed to improve participants' understanding of statistical concepts and provide practical tips for analyzing data. No specific video credits were mentioned.

Keywords

statistical tests

regression analyses

continuous data

ordinal data

categorical data

p-values

statistical significance

linear regression

logistic regression

sample size calculations