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Related Concept Videos

Introduction to Statistics01:17

Introduction to Statistics

The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

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A Guide to Basic Statistics for Educational Research.

Donna M Windish1

  • 1Associate Professor of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine; Director, Resident Research, Yale Primary Care Residency Program, Yale School of Medicine; Program Director, General Internal Medicine Medical Education Fellowship, Yale School of Medicine; Director, Advancement of Clinician-Educator Scholarship (ACES) Faculty Development Program, Department of Internal Medicine, Yale School of Medicine.

Mededportal : the Journal of Teaching and Learning Resources
|October 15, 2021
PubMed
Summary

This study demonstrates that specialized statistical training significantly boosts clinician-educators' confidence and knowledge in applying statistics to their scholarly work. The intervention improved statistical skills for medical education research and academic promotion.

Keywords:
Case-Based LearningFaculty DevelopmentQuantitative ResearchStatistics

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Area of Science:

  • Medical Education
  • Biostatistics
  • Faculty Development

Background:

  • Clinician-educators require statistical expertise for academic promotion and scholarly output.
  • Existing faculty development programs often lack comprehensive statistical training for evaluating educational interventions.

Purpose of the Study:

  • To assess the effectiveness of dedicated statistical training seminars for clinician-educators.
  • To improve faculty's ability to select and apply appropriate statistical tests in medical education scholarship.

Main Methods:

  • Five 90-minute seminars (in-person/virtual) were conducted for faculty at three academic centers.
  • Seminars included PowerPoint presentations on statistical tests and small-group practice sessions.
  • Post-seminar surveys evaluated participant feedback and self-rated statistical confidence.

Main Results:

  • 38 of 43 faculty (88%) completed evaluations, with 90% finding the session extremely useful.
  • Self-rated statistical confidence significantly increased post-seminar (3.00 vs. 1.97, p < .0001).
  • Most participants rated the session excellent, valuing small-group practice, though 69% desired more skills practice.

Conclusions:

  • Dedicated biostatistics training enhances clinician-educators' confidence and knowledge in statistical test selection for educational scholarship.
  • This intervention supports faculty development by equipping educators with essential skills for research evaluation.
  • Further development may involve expanding practice opportunities to address participant requests for more skills training.