This special edition episode closes out the year. Happy holidays, and thank you for your continued support. New weekly uploads resume on January 5, 2026.

Survival analysis is a core method in biostatistics used to study time-to-event data, especially in medical and public health research. In this episode, we break down survival analysis in simple terms, explain why it matters, and discuss how it’s used in real-world studies.

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This special edition episode closes out the year. Happy holidays, and thank you for your continued support. New weekly uploads resume on January 5, 2026.

The Salk vaccine was a major turning point in public health and epidemiology.

In this episode, we explain what the Salk vaccine was, why it was developed, how it helped stop polio, and how large-scale vaccine trials changed disease surveillance and public health practice.

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This special edition episode closes out the year. Happy holidays, and thank you for your continued support. New weekly uploads resume on January 5, 2026.

Healthy People 2030 is a national framework that guides public health goals across the United States—but what does it actually do, and why does it matter?

In this video, we break down Healthy People 2030 in simple terms, explain how it works, explore its key goals and focus areas, and show you what kind of data and resources you can find on the Healthy People 2030 website. 

Link to Healthy People 2030 Official Website: https://odphp.health.gov/healthypeople

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What does “degrees of freedom” really mean in biostatistics—and why does it matter?

In this episode, we break down degrees of freedom in simple terms, explain how it’s used in biostatistics, and walk through the formula step by step so you understand what each part represents. 

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Public health doesn’t just treat disease — it works to prevent it at every stage.
In this episode, you’ll learn how epidemiology uses three levels of prevention to reduce disease impact before it starts, detect it early, and manage long-term outcomes. Using a tuberculosis example, this episode walks step-by-step through how prevention works in real-world public health practice.

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Public health is changing fast, and technical skills are becoming more important than ever. In this episode, we break down how a Computer Science degree can support a Master of Public Health program and open doors to modern public health careers.

You’ll learn what each degree focuses on, the skills you gain from both programs, how Computer Science tools can be used during an MPH, and the types of careers this combination can lead to.

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How do epidemiologists decide which approach to use when studying health problems?

In this episode, you’ll learn what an epidemiological approach is, why it matters, and how it guides research decisions in public health. We’ll break down descriptive and analytical epidemiology, hypothesis generation, intervention and evaluation, and explain how study designs like randomized controlled trials, cohort studies, and case-control studies fit into the process. We’ll also touch on modern methods such as statistical modeling, systems dynamics, and Bayesian approaches.

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Parametric tests are some of the most commonly used tools in biostatistics. In this episode, you’ll learn what parametric tests are, why sampling matters, and how Z tests, T tests, and ANOVA are used in biostatistical analysis. This explanation focuses on concepts to help you build a strong foundation in biostatistics.

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Public health is founded on three key pillars: promotion, prevention, and protection. In this episode, you’ll learn what each pillar means, why they are important in public health practice, and how they are applied with simple, real-world examples. This beginner-friendly explanation shows how public health strategies impact daily life.

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In this episode, we break down Chi-square tests and regression analysis in biostatistics using clear, beginner-friendly explanations. You will learn the differences between simple and multiple regression, when to use a Chi-square test, and how confounding and effect modification can impact your results. 

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