Statistical Resources
If you or your team are interested in Statistical supports offered by the WHRI, please reach out to sabina.dobrer@cw.bc.ca.
Want to become a master in all things statistics and quantitative research? Explore our Sabina’s Stats Series where Senior Statistician, Sabina Dobrer covers fundamentals of statistics to unpacking linear and logistical regressions.
Guidance and Tools from Research Ideation to Publication
In below examples we use the term “woman” throughout as people assigned female at birth, including trans, nonbinary and gender diverse people, and cisgender women.
Sabina's Tips & Stats Corner
Complementing the lecture series, a collection of practical ‘Stats Tips’ was developed to reinforce core concepts and provide just-in-time guidance for common statistical challenges. Many of these tips were inspired by recurring questions and real-world examples encountered while preparing for or delivering the lectures. They serve as accessible, stand-alone resources that echo the structure of the Analytical Framework—bridging theory and application across data preparation, exploratory analysis, and modeling — while offering learners additional clarity and confidence as they progress through the material.
What is a Box Plot?
A box plot, also known as a box-and-whisker plot, is a standardized way of displaying the distribution of data based on a five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. It is particularly useful for identifying outliers and understanding the spread and skewness of the data.
Read more on Understanding Box Plots: A Comprehensive Guide Part 2.
How do you improve Statistical Inference?
When a sample is not selected based on probability and individuals select themselves into a group, self-selection bias arises. This is a known limitation of self-selection sampling, which can be defined as xxxxx. The main issues with non-probability sampling are:
- The samples do not represent the target population.
- There is an element of unknown sample inclusion or participation mechanisms.
Steps to create an Effective Survey
You don’t have to be an expert to create a survey, but by following a few survey best practices, you can make sure you’re collecting the best data possible.
Explore some essential points to consider when preparing the survey.
Normal Distribution and Central Limit Theorem
The Central Limit Theorem operates akin to a magical recipe for transforming messy data into a neatly structured, normal-looking graph (bell curve). Envision having an assortment of random entities, such as people’s heights or test scores.
Learn more on Normal Distribution and Central Limit Theorem.
Tests for Comparison of Categorical Variables
With the different types of data and the number of categories tested require different tests.
Start learning the basic tests for Comparison of Categorical Variables.
Paired or Not Paired?
What are the options for comparing categorical and continuous variables? What to use? Paired? Not paired?
This page provides guidelines for researchers and their teams on the appropriate tests to run to compare continuous variables.
What is linear regression?
Simple linear regression is used to estimate the relationship between two quantitative variables. You can use simple linear regression when you want to know:
How strong the relationship is between two variables
The value of the dependent (continuous) variable at a certain value of the independent variable (any type).
Statistics Sessions Video Library
Lectures presented below are aligned with the WHRI Analytical Framework, these statistical lectures are designed to guide learners through a structured progression — from foundational data management and preparation to increasingly advanced analytical techniques. Beginning with essential statistical concepts such as distributions, p-values, and summary measures, the lectures then introduce descriptive analytics (including common statistical tests), leading to the widely used modeling approaches such as linear regression, logistic regression, and survival analysis.
