Headlines with numbers confuse you
Doubles the risk of what, exactly?
Courses
Start with three things you can do today
How to understand statistics in the news
Read a statistic and know what it is really saying.
A course in statistical thinking for readers, not researchers. It covers what averages hide, how samples can mislead, what significant does and does not mean, how risk gets exaggerated, and the questions to ask of any number before believing it.
Want new features first?
A starting goal
I want to understand the statistics I meet in the news and at work: averages, samples, significance and risk. Teach me what these numbers actually mean and when to doubt them.
Change any word so it fits your situation. The course is built from what you type and a few setup questions.
Start today
Three things you can do before the course exists.
Useful on their own. The course is for when you want the whole skill, not just the first move.
-
Turn a percentage headline into counts
Try an invented example: delayed buses rise from 2 in every 100 journeys to 3 in every 100. That is a 50% relative increase, but a rise of 1 percentage point, or one additional delay per 100 journeys. Both descriptions are correct. Ask what was counted, over which period, and whether the journeys are comparable.
-
Calculate two averages before choosing one
For five invented waiting times of 5, 6, 7, 8 and 34 minutes, the mean is 60 divided by 5, or 12 minutes. The median is the middle value, 7. The long wait pulls the mean upward. Neither number is a lie: the mean describes the total per visit; the median describes the middle visit. Ask which answers your question.
-
Read the question behind a survey result
Compare these invented questions: "Should the library open on Sundays?" and "Should the library open on Sundays if weekday hours are reduced?" They ask about different tradeoffs. A change in the answers would not, by itself, show that public opinion changed. Before comparing two polls, check the exact wording, who was asked and how respondents were selected.
Sources: Office for National Statistics: why the mean and median can tell different stories; Pew Research Center: how survey wording affects answers. Practical examples are starting points, not promised results.
- Best for
- Math & numbers
- Prerequisites
- None. Bring the situation you have.
- Course length
- 7 to 30 lessons
- Pace
- Yours. One lesson at a time, as often as you like.
- Format
- Read it, or play the audio
- Access
- Available on the App Store and Google Play, with an optional early testing list
Before the course is built
Setup asks about your situation first.
Lesson 1 opens on the moment you describe, and the examples use your role. For this request, expect questions like these.
- Where do you meet statistics most: news, work reports, health claims, or research?
- What confuses you most: averages, percentages, significance, or risk?
- Do you want to read statistics better, or produce them too?
Right for now
If any of this sounds familiar, start here.
You nod at reports you do not follow
Averages, confidence and significance, and no idea what they mean.
You want to spot bad numbers
Not to be fooled by a chart or a claim.
Course arc
Where the course goes, in order.
Cadence writes the lessons for you, so the exact path follows your answers. This is the ground a course built from this sentence covers.
01 / 06Averages
Mean, median and the shape of the data behind them.
Samples
Who was asked, how many, and who was missed.
Correlation and cause
Why two things moving together proves less than it seems.
Significance
What the word means in research, and why it is so often misread.
Risk
Relative, absolute, and the framing that scares you.
Charts
Axes, scales and the tricks that mislead.
What changes
What you will be able to do, and what you will stop believing.
You will be able to
See what averages hide
Mean, median and spread, and which summary answers your question.
Judge a sample
Size, selection and why a survey of friends may not represent the population you care about.
Understand significance and uncertainty
What a p-value means, what it does not, and error bars in plain terms.
Read risk properly
Relative versus absolute, and why relative changes need the original absolute risk for context.
Ideas this course corrects
Statistically significant means important
A small p-value describes incompatibility with a specified statistical model; it does not establish practical importance or the probability that chance caused the result.
A large sample is always trustworthy
A larger sample can reduce random sampling error, but it does not by itself correct selection bias. Check how people were chosen as well as how many answered.
Course questions
What to know before you start.
How to understand statistics in the news: what can I do today?
Turn a percentage headline into counts. Try an invented example: delayed buses rise from 2 in every 100 journeys to 3 in every 100. That is a 50% relative increase, but a rise of 1 percentage point, or one additional delay per 100 journeys. Both descriptions are correct. Ask what was counted, over which period, and whether the journeys are comparable. Calculate two averages before choosing one. For five invented waiting times of 5, 6, 7, 8 and 34 minutes, the mean is 60 divided by 5, or 12 minutes. The median is the middle value, 7. The long wait pulls the mean upward. Neither number is a lie: the mean describes the total per visit; the median describes the middle visit. Ask which answers your question. Read the question behind a survey result. Compare these invented questions: "Should the library open on Sundays?" and "Should the library open on Sundays if weekday hours are reduced?" They ask about different tradeoffs. A change in the answers would not, by itself, show that public opinion changed. Before comparing two polls, check the exact wording, who was asked and how respondents were selected.
Is this a fixed course?
No. Cadence builds the course when you type the sentence and answer a few setup questions, so the lessons follow your situation. This page describes what a course built from this request covers. You see the whole path before Lesson 1.
Who is this course for?
A course in statistical thinking for readers, not researchers. It covers what averages hide, how samples can mislead, what significant does and does not mean, how risk gets exaggerated, and the questions to ask of any number before believing it. It sits under Math & numbers in the Ideas & discovery part of the library.
Do I need any prior experience?
No. Setup asks what you already know and where you want to use it, and the course starts from there. Bring a real situation if you have one; the examples are built around it.
How long does it take?
Most courses run 7 to 30 lessons. The course length is sized to what you asked for. You set the pace: one lesson at a time, as often as you like. Lessons can be read or played as audio.
Does this course award a certificate or degree?
No. Cadence is for practical self-learning and does not award accredited degrees or professional certification.
Start today
Type it, answer a few questions, open Lesson 1.
A starting goal
I want to understand the statistics I meet in the news and at work: averages, samples, significance and risk. Teach me what these numbers actually mean and when to doubt them.
Want new features first?