> For the complete documentation index, see [llms.txt](https://whri.gitbook.io/whristatresources.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whri.gitbook.io/whristatresources.com/videos/videos.md).

# Videos

This page includes detailed description of the videos related to statistics, data and analysis

YouTube Channel: \
<https://www.youtube.com/playlist?list=PLBXuEFdtqeB_6ZDSaOg7HSQabzwiCZQVY><br>

On this page, the videos are arranged in a logical progression, beginning with foundational concepts and gradually moving to more advanced topics.

{% hint style="info" %}
Depending on the browser, not all tabs can be seen on screen. \
Click on three dots ... on the right to get to additional tabs\
Currently included: General Concepts, Basic Concepts, Introduction to Modelling, Advanced Statistical Concepts
{% endhint %}

***

{% tabs %}
{% tab title="General concepts" icon="chair-office" %}

<table><thead><tr><th width="497">Description</th><th>Link</th></tr></thead><tbody><tr><td><ul><li><strong>Data Life Cycle</strong><br>Learn data lifecycle practices, from planning and production to protection and governance. Understand how data privacy, quality, and management support reliable analysis and informed decisions.</li></ul></td><td><a href="https://www.youtube.com/watch?v=Mm8HW-grD5c&#x26;t=7s">https://www.youtube.com/watch?v=Mm8HW-grD5c&#x26;t=7</a></td></tr><tr><td><ul><li><strong>What is Stats? A Series of Un/fortunate Events</strong><br>This lecture covers the two core concepts of statistics, probability and uncertainty, and the difference between epistemic and aleatoric uncertainty. I also talk about why correlation is not causation, the ethical responsibilities statisticians carry toward their data and their clients, and how classical, empirical, and subjective probability can lead to different conclusions from the same question.<br></li></ul></td><td><a href="https://www.youtube.com/watch?v=SX5eKNole5Y">https://www.youtube.com/watch?v=SX5eKNole5Y</a></td></tr><tr><td><ul><li><strong>Data Visualization -  Crafting your story</strong><br>Data visualization is used by researchers on the daily basis. It is not only a design skill but a tool  to validate data, explore it before modeling, and finally tell its story. A well-chosen plot can reveal a distribution, catch an outlier, or check a model's assumptions faster than any table of numbers, and it can also invite the person looking at it to ask the next question rather than simply hand them an answer.<br><br><br></li></ul></td><td><a href="https://www.youtube.com/watch?v=h9WJRETYcMQ">https://www.youtube.com/watch?v=h9WJRETYcMQ</a></td></tr></tbody></table>
{% endtab %}

{% tab title="Basic concepts" icon="chair-office" %}

<table><thead><tr><th width="498">Description</th><th>Link</th></tr></thead><tbody><tr><td><ul><li><strong>Statistical Distributions – Why Are They Important?</strong><br>This lecture explains how the type of variable -  continuous, categorical, ordinal, discrete, or binary - shapes the way data are summarized, analyzed, presented, and interpreted. It then connects variable types to commonly used probability distributions, including the normal, binomial, and Poisson distributions. Using detailed examples and calculations, the lecture demonstrates how distributions describe the behaviour of data and guide the selection of appropriate statistical methods.</li></ul></td><td><a href="https://www.youtube.com/watch?v=TGOkEQePksM&#x26;t=11s">https://www.youtube.com/watch?v=TGOkEQePksM&#x26;t=11s</a></td></tr><tr><td><ul><li><strong>Unveiling Your Findings – Going Beyond the P-Value Hype</strong><br>This lecture explains the role of p-values in hypothesis testing and the limitations of relying on them alone. It introduces effect sizes, confidence intervals, and practical significance, showing how these measures support a more complete interpretation and presentation of research findings.</li></ul></td><td><a href="https://www.youtube.com/watch?v=rNjmayaquQ4">https://www.youtube.com/watch?v=rNjmayaquQ4</a></td></tr><tr><td><ul><li><strong>Navigating the Data Jungle Demystifying – Proportions, Rates, Ratios, &#x26; Relative Risk</strong><br>This lecture explains the differences among proportions, rates, ratios, prevalence, incidence, and relative risk. It shows how these measures are calculated, interpreted, and applied in epidemiological and health research. Practical examples demonstrate why selecting and defining the appropriate measure is essential for avoiding misinterpretation and drawing valid conclusions.</li></ul></td><td><a href="https://www.youtube.com/watch?v=qWxlEmGhh6E&#x26;t=1s">https://www.youtube.com/watch?v=qWxlEmGhh6E&#x26;t=1s</a></td></tr><tr><td><ul><li><strong>Comprehensive Insights: Understanding the Link Between Study Designs, Statistical Modeling, and Analytical Applications</strong><br>This lecture explains the essential connections among research questions, study design, data structure, and statistical analysis. It demonstrates why analytical methods cannot correct fundamental problems in how a study was designed or documented. Practical examples show how early statistical involvement and careful planning support valid analysis, accurate interpretation, and successful publication.</li></ul></td><td><a href="https://www.youtube.com/watch?v=hy4UbnF23Hc&#x26;t=8s">https://www.youtube.com/watch?v=hy4UbnF23Hc&#x26;t=8s</a></td></tr></tbody></table>
{% endtab %}

{% tab title="Introduction to Modelling" icon="chair-office" %}

<table><thead><tr><th width="503">Description</th><th>Link</th></tr></thead><tbody><tr><td><ul><li><strong>Parametric vs. Non-Parametric: The Stat Battle</strong><br>This lecture explains the differences between parametric and non-parametric methods, moving beyond the simple distinction based on normality. It examines how rank-based tests work, the assumptions underlying both approaches, and why non-parametric tests do not necessarily compare medians. The lecture also introduces stochastic superiority and explains how the research question, study design, variable type, and intended interpretation should guide the choice of method.</li></ul></td><td><a href="https://www.youtube.com/watch?v=aZTQKWDmG64&#x26;t=114s">https://www.youtube.com/watch?v=aZTQKWDmG64&#x26;t=114s</a></td></tr><tr><td><ul><li><strong>Power in Simplicity: Unpacking Linear &#x26; Logistic Regression</strong><br>This lecture introduces linear and binary logistic regression and explains when each model should be used. It covers the interpretation of model coefficients and key assumptions, including linearity and homoscedasticity in linear regression. The lecture demonstrates why model selection, assumption checking, and careful interpretation are essential for producing valid and meaningful results.</li></ul></td><td><a href="https://www.youtube.com/watch?v=gvYPeYX4bjs&#x26;t=1s">https://www.youtube.com/watch?v=gvYPeYX4bjs&#x26;t=1s</a></td></tr><tr><td><ul><li><strong>Survival Analysis: Mastering the Essentials</strong><br>This lecture introduces the foundations of survival analysis and explains how time-to-event data differ from other types of outcomes. It covers censoring, Kaplan–Meier survival curves, group comparisons, and Cox proportional hazards regression. Using practical examples, the lecture demonstrates how survival probabilities and hazard ratios are estimated and interpreted, while emphasizing the importance of the proportional hazards assumption.</li></ul></td><td><a href="https://www.youtube.com/watch?v=8p1V1LZXJGg&#x26;t=1s">https://www.youtube.com/watch?v=8p1V1LZXJGg&#x26;t=1s</a></td></tr></tbody></table>
{% endtab %}

{% tab title="Advanced Statistical Concepts" icon="chair-office" %}

<table><thead><tr><th width="502">Description</th><th>Link</th></tr></thead><tbody><tr><td><ul><li><strong>Who Counts? Denominators, Bias, and the Illusion of Equity</strong><br>This lecture examines how the choice of denominator, population definitions, and comparison groups can shape research findings and create an illusion of equity. It introduces confounding, counterfactual reasoning, the Rubin Causal Model, Pareto dominance, and mathematical fairness to explain why apparently similar groups may not support valid causal comparisons. Through practical examples, it shows how selection, measurement, aggregation, and structural bias affect who is represented, what is observed, and whether an intervention is truly beneficial, and for whom.</li></ul></td><td><a href="https://www.youtube.com/watch?v=zjakvEWA6WY&#x26;t=2s">https://www.youtube.com/watch?v=zjakvEWA6WY&#x26;t=2s</a></td></tr><tr><td><ul><li><strong>When Trends Matter: Rethinking Before-After Analyses</strong><br>This lecture introduces interrupted time series (ITS) and ARIMA models and explains how they address different questions involving data measured over time. It shows how ITS evaluates changes in level and trend following an intervention, while ARIMA models autocorrelation, seasonality, and temporal patterns for explanation and forecasting. Practical research examples demonstrate when each approach is appropriate and how they can be combined to strengthen the analysis.</li></ul></td><td><a href="https://www.youtube.com/watch?v=aqBSTlnHS7o&#x26;t=3s">https://www.youtube.com/watch?v=aqBSTlnHS7o&#x26;t=3s</a></td></tr><tr><td></td><td></td></tr></tbody></table>
{% endtab %}
{% endtabs %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://whri.gitbook.io/whristatresources.com/videos/videos.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.