A Conversation on Causality at Our Table (EN)

Brewing coffee, exchanging words. As steam rises, time drifts, and the world of statistics opens little by little.

This site grew from small coffee-chat conversations between a clinician daughter and her statistician father. Questions that surface in research, numbers that catch while reading a paper, and occasional glimpses of a researcher’s stance—the things that truly matter tend to lie scattered quietly across everyday life, clinical practice, causality, data, cognition, and language.

Over coffee, their light exchanges pick up some of these fragments, one by one—woven into a single story, as if careful not to disturb their totality.

Told in words warm enough to hold on a winter morning, the narrative moves with a spiral of quiet foreshadowing. Mathematics and medicine appear along the way, but take only what you need. Some episodes include short R scripts—small experiments you can rerun, so the ideas remain reproducible and tangible. If you follow the road all the way to the morning a paper is finally ready to submit, scattered meanings begin to connect—somewhere, into something whole.

How to begin

This story follows a single clinical study, moving from research hypotheses to statistics, effects, causality, and language. If you are not sure where to start, these three episodes are good entry points:

Each episode can be read on its own—feel free to choose whichever layer interests you.

R scripts for reproducing the episodes

If you came here from an R post, you may want the code first. These scripts are meant to reproduce the figures and ideas, not to teach programming.

Study Design: study-design.R
Frequentist Thinking / Experiments: frequentist.R
Effects and Time: effects.R

Episodes

1. Study Design — Where research begins

Every study starts with a question. How do we frame PICO/PECO? How do we choose outcomes? How do we protect against bias? The daughter asks her father about the very starting point of clinical research.

A glimpse ahead

A Story of Coffee Chat and Research Hypothesis Research often stumbles first not on data, but on how a question is framed.
Data Have Types: A Coffee-Chat Guide to R Functions for Common Outcomes Numbers may look the same, yet describe different worlds—this pause starts from outcome types and the probability models behind them.
Outcomes: The Bridge from Data Collection to Analysis Once an outcome is defined, much of the study is already decided—pausing over that choice through familiar oncology examples.
A First Step into Survival and Competing Risks Analysis with R Before analysis begins, trouble often begins with how events are defined and coded—thinking in terms of time and events.
When Bias Creeps In: Selection, Information, and Confounding in Clinical Surveys Distortion rarely comes from formulas alone—this is where bias quietly enters.

2. Glossary — The words of statistics

Statistical Terms in Plain Language

3. Frequentist Thinking — Practicing statistics

Using cancer clinical trials as examples, the father explains the frequentist perspective: “How do numbers behave under repeated sampling?”

A glimpse ahead

Reading a Paper over a Cup of Coffee The unease felt while reading a paper often comes from language — pausing over statistical terms.
P-Value Explanations That Seem Plausible at First Glance What would happen if the same study were repeated again and again — touching the idea of frequentist thinking.
Beyond 0.05: Interpreting P-Values in a Clinical Trial A p-value is less a number than a shadow of design — watching where interpretation drifts.

4. Frequentist Experiments — Checking statistical ideas by simulation

The ideas behind frequentist statistics are not just abstractions; they can be checked through simulation. Even if you are unfamiliar with R, you can run the script and reproduce the results.

A glimpse ahead

Understanding Confidence Intervals via Hypothetical Replications in R Where does “95%” come from, and where does it go — lingering over confidence intervals.
Alpha, Beta, and Power: The Fundamental Probabilities Behind Sample Size Sample size planning is also error planning. This episode revisits the foundations of frequentist design.

5. Effects and Time — How effects evolve

We unravel risk differences, risk ratios, hazard ratios, vaccine effectiveness, attributable fractions, and effects that change over time alongside survival curves and cumulative incidence curves. A multidimensional perspective on data quietly develops, paving the way for causal inference.

A glimpse ahead

Silent Confusions Hidden in Percentages Percentages feel intuitive, yet they often mislead. Rethinking how effects are communicated.
Who Is This Percentage About—Target Populations and Attributable Fractions Every number speaks about someone — reconsidering the assumed population.
When Odds Ratios Approximate Risk Ratios—and When They Fail Measures that look similar may behave differently — pausing over the odds ratio.
From Risk and Rate to Survival and Hazard Once time enters the picture, effects change shape. An entry point to thinking about effects over time.
A First Note on Cox Regression A familiar hazard ratio rests on a modeling assumption — proportional hazards.
After Cox Regression: A Case Study and R Demonstration When hazards stop being proportional, the survival curve asks us to look at time again.

Coming next

Later English releases will continue into regression modeling, causal inference, and writing a paper. For now, the English conversation is published through Study Design, Frequentist Thinking, Frequentist Experiments, and Effects and Time.

This site shares selected R-related posts via R-bloggers

Reading a Paper over a Cup of Coffee

Have you ever felt lost in statistical terms the first time you read a paper? This episode begins with the daughter’s questions in front of a clinical trial article. Over coffee, her Dad quietly unpacks how to read a paper properly—starting with survival curves.

P-Value Explanations That Seem Plausible at First Glance

What is the true meaning of p-values in clinical trials? A coffee-chat guide to confirm the concept of hypothetical repetitions—the frequentist approach—through conversation. This piece cultivates an intuitive understanding of p-values.

Beyond 0.05: Interpreting P-Values in a Clinical Trial

To interpret p-values responsibly, you have to read the Methods—not just the Results. Using the American Statistical Association’s statement as a guide, this coffee-chat guide revisits what a p-value does (and does not) mean, and looks one step beyond 0.05.

Understanding Confidence Intervals via Hypothetical Replications in R

What does the 95% in a 95% confidence interval mean? This coffee-chat guide uses R simulation to make hypothetical repeated studies visible.

Alpha, Beta, and Power: The Fundamental Probabilities Behind Sample Size

Sample size planning is not just counting how many people are needed. It is a way of designing alpha error, beta error, power, and the effect size a study is meant to detect.

Silent Confusions Hidden in Percentages

Percentage displays are easy to understand, yet they can also lead to misunderstandings. Using vaccine effectiveness as an example, this coffee-chat guide explores why numbers can take on a life of their own. We’ll rethink how we communicate effectiveness.

Who Is This Percentage About? Target Populations and Attributable Fractions

Numbers can take on a life of their own not only because formulas are misunderstood, but also because the target population is hidden. This coffee-chat guide introduces attributable fractions and asks who a percentage is really about.

When Odds Ratios Approximate Risk Ratios—and When They Fail

Odds ratios are often taught as approximations to risk ratios, but that shortcut depends on the risk being small. This coffee-chat guide compares risk ratios, odds ratios, and the square root of the odds ratio.

A First Note on Cox Regression

Cox regression is not merely a machine for producing hazard ratios. This coffee-chat guide explains proportional hazards as the key modeling assumption behind the familiar output.

After Cox Regression: A Case Study and R Demonstration

When proportional hazards does not hold, a single hazard ratio can become hard to interpret. This coffee-chat guide uses a lung cancer trial and an R simulation to compare hazards, risks, and time-point effects.

Data Have Types: A Coffee-Chat Guide to R Functions for Common Outcomes

This post is part of a long-form series on how clinicians learn research and statistics, told through conversations over coffee. In this episode, a clinician daughter and her statistician father build a practical mental map from outcome types (continuous, binary, count, survival) to probability models and common R functions such as ggplot() and cifplot(). This article can be read on its own and serves as a gentle entry point to the series.

Dec 14, 2025