Analyze Healthcare Data with Confidence

Healthcare professionals make critical decisions every day that impact patient outcomes—but how confident can you be in your data-driven conclusions without proper statistical foundation? This Short Course was created to help healthcare data analysts accomplish reliable statistical analysis that directly supports patient care improvements. By completing this course, you'll be able to calculate meaningful confidence intervals for population estimates, identify and explain the two critical types of statistical errors that can impact healthcare decisions, and analyze relationships between categorical variables using proven statistical methods. By the end of this course, you will be able to: - Apply standard statistical functions to compute confidence intervals for a mean - Explain the difference between Type I and Type II errors in hypothesis testing - Analyze the relationship between categorical variables using a Chi-square test This course is unique because it bridges statistical theory with practical healthcare applications, giving you the confidence to make evidence-based recommendations that improve patient outcomes. To be successful in this project, you should have basic familiarity with healthcare data and elementary mathematical concepts

• Calculate confidence intervals to quantify uncertainty
• Understand Type I and II errors in healthcare decision-making
• Apply chi-square tests to assess categorical variable relationships
• Use rigorous statistical methods for reliable healthcare analysis

• Basic computer and internet skills
• Ability to read course instructions in English and complete short practice activities