Charts: choosing the right one

You

Alex, help me work through “Charts: choosing the right one” step by step.

Alex Tutor

A good chart answers one question at a glance. A bad chart makes the reader work out what the question was. So before you open the chart menu, write down what you want the reader to see. Nearly every product and marketing chart answers one of five questions:

Question	Chart
How are the values of one metric distributed?	Histogram, box plot
How do categories compare?	Bar chart
What share of a whole does each part have?	Bar chart; a pie only for 2–4 parts
Are two metrics related?	Scatter plot
How does a metric change over time?	Line chart

In this lesson we'll go through each of them with CatChow data: the 90-day customer spend from lesson 3, and a new dataset of weekly sessions and orders by marketing channel. At the end, you'll learn the most common ways charts mislead, so you can avoid them and spot them in other people's slides.

Alex Tutor

Dataset · 24 rows

90-day spend of new CatChow customers

The same 24 customers as in lesson 3. Synthetic data made for this course.

Download CSV

Columns
customer_id: Customer ID
spend_90d: Total spend in the first 90 days, USD
orders_90d: Number of orders in the first 90 days

Data

customer_id	spend_90d	orders_90d
U-5101	42	2
U-5102	85	3
U-5103	15	1
U-5104	28	1
U-5105	24	1

Show 19 more rows

U-5106	24	1
U-5107	64	2
U-5108	32	1
U-5109	56	2
U-5110	93	3
U-5111	26	1
U-5112	22	1
U-5113	20	1
U-5114	45	2
U-5115	110	4
U-5116	34	1
U-5117	50	2
U-5118	36	1
U-5119	38	1
U-5120	240	12
U-5121	56	2
U-5122	12	1
U-5123	18	1
U-5124	30	1

Alex Tutor

To see the shape of one metric, first build a frequency table: split the range of values into equal bins and count how many observations fall into each. The count is the absolute frequency; divide by the total and you get the relative frequency (a percentage). With bins of $50, the 24 customers look like this:

90-day spend	Customers	Share
0–49	16	67%
50–99	6	25%
100–149	1	4%
150–199	0	0%
200–249	1	4%

A histogram draws that table as touching bars: bin on the horizontal axis, count on the vertical. Bars touch because the bins are consecutive ranges of one continuous number. That's the visual difference from a bar chart of separate categories.

Bin width changes the story. Compare the chart below with the one in lesson 3, which used $20 bins on the same data. At $50 the whole 20–60 hump collapses into one bar, and you can no longer see where most customers actually are. Too narrow bins cause the opposite problem: lots of tiny bars and random noise. A useful start is 5–15 bins, then try a couple of widths before choosing. If you have many values, a frequency polygon (a line joining the tops of the bars) is a lighter alternative, and it lets you overlay two groups.

Alex Tutor

Question 1

Match each question to the chart that answers it best.

Tap an answer, then tap the row it belongs to. You can also drag.

How are order values distributed?
Which support topic gets the most tickets?
How did daily revenue change this quarter?
Do campaigns with a higher CTR also get more sign-ups?

Answers left to place

Alex Tutor

The same 24 customers with 50 USD binsWide bins hide the shape: compare with the 20 USD bins in lesson 3, where the peak at 20–40 USD and the gradual tail are visible.

Chart data

Spend in 90 days, USD	Customers
0–49	16
50–99	6
100–149	1
150–199	0
200–249	1

Alex Tutor

A box plot (box-and-whisker plot) squeezes a distribution into five numbers plus the outliers. For our 24 customers:

Element	What it shows	Value
Lower whisker	Smallest value inside the fences	$12
Bottom of the box	$Q_1$	$24
Line inside the box	Median	$35
Top of the box	$Q_3$	$56
Upper whisker	Largest value inside the fences ($104)	$93
Dots	Outliers beyond the fences	$110, $240

Reading it takes practice, but it pays off. The box is the middle 50% of customers. A median line close to the bottom of the box, and a long upper whisker with dots above it, signal a right skew, which is exactly what we found in lesson 3.

Box plots are at their best when you compare groups side by side. Picture lesson 2's couriers: courier A's box would be a thin sliver from 23 to 26 hours; courier B's a tall box from 18 to 30 with whiskers from 14 to 38. One glance, no averages needed. Box plots of delivery times by region, order values by channel, or session lengths by app version work the same way.

Alex Tutor

Question 2

You want to compare the distribution of delivery times for three couriers in one compact chart, including their medians and outliers. Which chart do you choose?

Choose one answerThree box plots side by sideA pie chart per courierA bar chart of the three mean timesA line chart

Alex Tutor

Dataset · 20 rows

CatChow sessions and orders by channel, 4 weeks

Weekly sessions and orders for five marketing channels. Synthetic data made for this course.

Download CSV

Columns
week_start: Monday the week starts on (YYYY-MM-DD)
channel: seo = organic search, meta_ads = paid social, google_ads = paid search, email = newsletter, referral = links from partner sites
sessions: Sessions on the site
orders: Orders placed

Data

week_start	channel	sessions	orders
2026-08-31	seo	4000	84
2026-08-31	meta_ads	2500	40
2026-08-31	google_ads	1800	54
2026-08-31	email	800	48
2026-08-31	referral	400	11

Show 15 more rows

2026-09-07	seo	4300	90
2026-09-07	meta_ads	2600	42
2026-09-07	google_ads	1900	57
2026-09-07	email	900	54
2026-09-07	referral	420	11
2026-09-14	seo	4100	86
2026-09-14	meta_ads	2700	46
2026-09-14	google_ads	1850	55
2026-09-14	email	850	51
2026-09-14	referral	380	10
2026-09-21	seo	4500	92
2026-09-21	meta_ads	2700	44
2026-09-21	google_ads	1950	58
2026-09-21	email	950	57
2026-09-21	referral	400	10

Alex Tutor

Question 3

Starting a bar chart's value axis at 80 instead of 0 is a harmless way to make small differences easier to see.

TrueFalse

Alex Tutor

Bar charts compare a number across categories: orders by channel, revenue by product line, tickets by reason. A few rules make them much easier to read:

Sort the bars by value, unless the categories have a natural order (like months or plan tiers).
Start the value axis at zero. A bar's length is the message. Cut the axis and the lengths lie.
Use horizontal bars when the labels are long, so people don't have to tilt their heads.
Label directly instead of forcing the eye back and forth to a legend.

The chart below compares two shares for each channel over four weeks: its share of sessions and its share of orders. The question it answers is "Which channels bring more orders than their traffic would suggest?"

Alex Tutor

Email brings 9% of sessions but 21% of ordersShare of sessions vs share of orders by channel, four weeks combined. Channels are sorted by number of orders.

Chart data

Channel	Share of sessions, %	Share of orders, %
SEO	42.25	35.2
Google Ads	18.75	22.4
Email	8.75	21
Meta Ads	26.25	17.2
Referral	4	4.2

Alex Tutor

Question 4

Using the dataset CatChow sessions and orders by channel, 4 weeks: what share of all orders over the four weeks came from email?

Your answer

Units: %, e.g. 12.5

Alex Tutor

A pie chart shows how a whole splits into parts. It works when there are two to four parts and one of them clearly dominates ("62% of orders come from mobile"). Beyond that it falls apart: people compare angles and areas far less accurately than lengths, so 17% and 21% slices look the same, and with seven slices nobody can rank them. Never use a 3D pie; the tilt distorts the slice sizes.

For most "share" questions a sorted bar chart is simply better, like the one above. If you need to show both the parts and the total, a single stacked bar also works.

Alex Tutor

To see whether two numbers move together, put one on each axis and draw one dot per observation. That's a scatter plot. Below, each dot is one channel in one week: sessions on the horizontal axis, orders on the vertical.

Notice what the scatter reveals that a table hides: the dots don't form one cloud but clusters, one per channel. More sessions generally mean more orders, but at very different rates. The email dots sit high above the others despite having few sessions. When a scatter shows clusters, the grouping variable (here, the channel) is usually part of the story.

Need a third variable? A bubble chart makes the dot size encode it (for example, ad spend). Use it sparingly: bubble areas are hard to compare precisely. A scatter plot shows a relationship, not a cause. Module 5 is entirely about that difference.

Alex Tutor

Question 5

Using the dataset 90-day spend of new CatChow customers, build a frequency table with $25 bins (0–24, 25–49, 50–74, …). How many customers fall into the 25–49 bin?

Your answer

Units: whole number, e.g. 7

Alex Tutor

Weekly sessions and orders: each dot is one channel in one weekFive clusters, one per channel. The four email weeks (about 800–950 sessions, 48–57 orders) convert far better than the four Meta Ads weeks (about 2,500–2,700 sessions, 40–46 orders).

Chart data

Sessions per week	Orders
4,000	84
2,500	40
1,800	54
800	48
400	11

Show 15 more rows

4,300	90
2,600	42
1,900	57
900	54
420	11
4,100	86
2,700	46
1,850	55
850	51
380	10
4,500	92
2,700	44
1,950	58
950	57
400	10

Alex Tutor

For a metric tracked over time, use a line chart: time runs left to right, and the line's slope shows direction and speed at a glance. Keep the time steps even (don't mix days and weeks on one axis), and mark events that explain jumps, such as a launch, a sale or a tracking change.

The chart below shows CatChow's total weekly orders. Notice the value axis: it starts at zero. Total orders grew from 237 to 261, about 10% in four weeks. That's real, steady growth, and the chart shows it at its true size.

Alex Tutor

Question 6

Which of these practices make a chart misleading? Select all that apply.

Choose all that applyShowing "80% of users loved it" without saying 4 of 5 users were askedSorting bars from largest to smallestStarting the chart just after an unusually bad week to show "growth"Labelling bars directly with their valuesUsing two y-axes with different scales to make two lines appear to move together

Alex Tutor

Weekly orders grew about 10% in four weeks

Chart data

Week starting	Orders, all channels
2026-08-31	237
2026-09-07	254
2026-09-14	248
2026-09-21	261

Alex Tutor

Charts feel objective, which makes them powerful tools for persuasion, honest or not. These are the tricks you'll meet most often. Don't use them, and learn to spot them:

A truncated axis. Draw the same weekly orders on an axis that starts at 230 instead of 0, and the rise from 237 to 261 looks like the line has quadrupled. For bar charts it's always misleading, because bar length is the message. For line charts a non-zero start can be fine, as long as it's clearly labelled and the change is meaningful at its real size.
Percentages without the base. "60% of vets recommend our new food" sounds great, until you learn that 3 of 5 vets were asked. Always show n next to a percentage.
A cherry-picked time window. Start the chart right after a bad week and any trend looks like growth. Show enough history for the reader to judge.
Drowning a series. Put a small metric on the same axis as one ten times bigger and its changes disappear. That can hide a real problem (like refunds doubling) behind a big, calm line.
Stretching or squeezing the axes. The same data looks dramatic on a narrow, tall chart and flat on a wide, short one. Keep proportions honest and similar across charts you want people to compare.
Dual axes. Two lines with two different scales can be made to cross, diverge or move together at will. Use two separate charts instead.

Clutter misleads too, just more quietly. Remove anything that carries no information (3D effects, heavy gridlines, decorative icons), keep colours to about three, and use colour to highlight what matters, not to decorate.

Alex Tutor

Question 7Short answer · AI-checked task

A colleague's slide shows traffic by source as a 3D pie chart with nine slices in nine colours, titled "Traffic sources". Suggest how to improve it in 2–4 sentences.

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Alex Tutor

Does the chart answer one clear question, and does the title state the answer?
Is it the right type for that question (distribution, comparison, share, relationship, trend)?
Does the bar axis start at zero? Are the time steps even?
Is the sample size visible wherever you show percentages or averages?
Can you remove anything without losing information? Then remove it.
If you show averages with error bars, does the caption say whether they are SD, standard error or a confidence interval? (You'll meet the last two in modules 2 and 3.)

That wraps up module 1. You can now describe any metric with a typical value, a spread, a clear view of its shape and outliers, and an honest chart. Module 2 moves from describing data to comparing groups: when is a difference between two numbers real, and when is it just noise?

Alex Tutor

Question 8Case · AI-checked task

Describe the one chart you'd put on the slide: its type, what goes on each axis, the title, and one caveat you'd add underneath.

CatChow's CMO is preparing next quarter's budget and asks you for one chart for the leadership meeting that answers: "Which channel deserves more investment?" Leadership will look at the slide for about 30 seconds.

You have four weeks of sessions and orders by channel (dataset CatChow sessions and orders by channel, 4 weeks). A teammate has already drafted a pie chart of sessions by channel.

Dataset for this task: CatChow sessions and orders by channel, 4 weeks

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Alex Tutor

That’s the lesson. You answered every task — nicely done.

Next lesson: Samples, populations and the normal distribution