Episode 13 | Survival Analysis: Making Sense of Time-to-Event Data
In this episode, we introduce the core ideas behind analyzing time-to-event data—situations where the outcome isn’t just “what happened,” but when it happened. A key challenge is that some participant...
3 Helmi 41min
Episode 12 | Clustering and Classification: Finding Structure in Data
In this episode, we step into multivariate thinking and ask a practical question: when do data points naturally form “groups,” and how can we use those groups to make decisions?We walk through how gro...
3 Helmi 38min
Episode 11 | Finding Structure in Multivariate Data
This episode is about what to do when your data has many variables at once. We start with the basic idea of how variables “move together” (correlation and covariance), and why that matters for underst...
2 Helmi 44min
Episode 10 | From Chi-Square to GLMs: Beyond Linear Regression
This episode is about working with categorical outcomes—questions where results fall into categories rather than a numeric scale. We learn how to check whether two variables are related, how to model ...
2 Helmi 36min
Episode 9 | Categorical Data in Practice: Measures of Association, and Simpson’s Paradox
In this episode, we start with Fisher’s “Lady Tasting Tea”—a classic reminder that good questions need good experimental design. Then we shift from continuous outcomes to categorical data: how a simpl...
2 Helmi 41min
Episode 8 | Two-Way ANOVA and Beyond
This episode moves from one-way ANOVA to two-factor randomized experiments, focusing on how to test main effects and, more importantly, interactions—when the effect of one factor depends on the level ...
1 Helmi 36min
Episode 7 | Design of Experiments
This episode introduces the core logic of experimental design and ANOVA: what we mean by causality, factors, and confounders—and why randomization, replication, and blocking are the practical tools th...
1 Helmi 31min
Episode 6 | Model Selection Strategies
Episode 6 is about making multiple regression work in real life: how to choose predictors without overfitting, when to transform variables to fix messy variance or nonlinearity, and what to do when pr...
1 Helmi 37min
