Did prices go up or down? Depends on how one looks at the data

The U.S. media have been flooded with reports of runaway inflation recently, and it's refreshing to see a nice article in the Wall Street Journal that takes a second look at the data. Because as my readers know, raw data can be incredibly deceptive.

Inflation typically describes the change in price level relative to the prior year. The month-on-month change in price levels is a simple seasonal adjustment used to remove the effect of seasonality that masks the true change in price levels. (See this explainer of seasonal adjustment.)

As the pandemic enters the second year, this methodology is comparing 2021 price levels to pandemic-impacted price levels of 2020. This produces a very confusing picture. As the WSJ article explains, prices can be lower than they were in 2019 (pre-pandemic) and yet substantially higher than they were in 2020 (during the pandemic). This happens in industry sectors that were heavily affected by the economic shutdown, e.g. hotels, travel, entertainment.

Wsj_pricechangehotels_20192021Here is how they visualized this phenomenon. Amusingly, some algorithm estimated that it should take 5 minutes to read the entire article. It may take that much time to understand properly what this chart is showing.

Let me save you some time.

The chart shows monthly inflation rates of hotel price levels.

The pink horizontal stripes represent the official inflation numbers, which compare each month's hotel prices to those of a year prior. The most recent value for May of 2021 says hotel prices rose by 9% compared to May of 2020.

The blue horizontal stripes show an alternative calculation which compares each month's hotel prices to those of two years prior. Think of 2018-9 as "normal" years, pre-pandemic. Using this measure, we find that hotel prices for May of 2021 are about 4% lower than for May of 2019.

(This situation affects all of our economic statistics. We may see an expansion in employment levels from a year ago which still leaves us behind where we were before the pandemic.)

What confused me on the WSJ chart are the blocks of color. In a previous chart, the readers learn that solid colors mean inflation rose while diagonal lines mean inflation decreased. It turns out that these are month-over-month changes in inflation rates (notice that one end of the column for the previous month touches one end of the column of the next month).

The color patterns become the most dominant feature of this chart, and yet the month-over-month change in inflation rates isn't the crux of the story. The real star of the story should be the difference in inflation rates - for any given month - between two reference years.


In the following chart, I focus attention on the within-month, between-reference-years comparisons.


Because hotel prices dropped drastically during the pandemic, and have recovered quite well in recent months as the U.S. reopens the economy, the inflation rate of hotel prices is almost 10%. Nevertheless, the current price level is still 7% below the pre-pandemic level.



Start at zero improves this chart but only slightly

The following chart was forwarded to me recently:


It's a good illustration of why the "start at zero" rule exists for column charts. The poor Indian lady looks extremely short in this women's club. Is the average Indian woman really half as tall as the average South African woman? (Surely not!)

Junkcharts_redo_womenheight_columnThe problem is only superficially fixed by starting the vertical axis at zero. Doing so highlights the fact that the difference in average heights is but a fraction of the average heights themselves. The intra-country differences are squashed in such a representation - which works against the primary goal of the data visualization itself.

Recall the Trifecta Checkup. At the top of the trifecta is the Question. The designer obviously wants to focus our attention on the difference of the averages. A column chart showing average heights fails the job!

This "proper" column chart sends the message that the difference in average heights is noise, unworthy of our attention. But this is a bad take of the underlying data. The range of average heights across countries isn't that wide, by virtue of large population sizes.

According to Wikipedia, they range from 4 feet 10.5 to 5 feet 6 (I'm ignoring several entries in the table based on non representative small samples.) How do we know that the difference of 2 inches between averages of South Africa and India is actually a sizable difference? The Wikipedia table has the average heights for most of the world's countries. There are perhaps 200 values. These values are sprinkled inside the range of about 8 inches top to bottom. If we divide the full range into 10 equal bins, that's roughly 0.8 inches per bin. So if we have two numbers that are 2 inches apart, they almost span 2 bins. If the data were evenly distributed, that's a huge shift.

(In reality, the data should be normally distributed, bell-shaped, with much more at the center than on the edges. That makes a difference of 2 inches even more significant if these are normal values near the center but less significant if these are extreme values on the tails. Stats students should be able to articulate why we are sure the data are normally distributed without having to plot the data.)


The original chart has further problems.

Another source of distortion comes from the scaling of the stick figures. The aspect ratio is being preserved, which means the area is being scaled. Given that the heights are scaled as per the data, the data are encoded twice, the second time in the widths. This means that the sizes of these figures grow at the rate of the square of the heights. (Contrast this with the scaling discussed in my earlier post this week which preserves the relative areas.)

At the end of that last post, I discuss why adding colors to a chart when the colors do not encode any data is a distraction to the reader. And this average height chart is an example.

From the Data corner of the Trifecta Checkup, I'm intrigued by the choice of countries. Why is Scotland highlighted instead of the U.K.? Why Latvia? According to Wikipedia, the Latvia estimate is based on a 1% sample of only 19 year olds.

Some of the data appear to be incorrect (or the designer used a different data source). Wikipedia lists the average height of Latvian women as 5 ft 6.5 while the chart shows 5 ft 5 in. Peru's average height of females is listed as 4 ft 11.5 and of males as 5 ft 4.5. The chart shows 5 ft 4 in.


Lest we think only amateurs make this type of chart, here is an example of a similar chart in a scientific research journal:


(link to original)

I have seen many versions of the above column charts with error bars, and the vertical axes not starting at zero. In every case, the heights (and areas) of these columns do not scale with the underlying data.


I tried a variant of the stem-and-leaf plot:


The scale is chosen to reflect the full range of average heights given in Wikipedia. The chart works better with more countries to fill out the distribution. It shows India is on the short end of the scale but not quite the lowest. (As mentioned above, Peru actually should be placed close to the lower edge.)


Plotting the signal or the noise

Antonio alerted me to the following graphic that appeared in the Economist. This is a playful (?) attempt to draw attention to racism in the game of football (soccer).

The analyst proposed that non-white players have played better in stadiums without fans due to Covid19 in 2020 because they have not been distracted by racist abuse from fans, using Italy's Serie A as the case study.


The chart struggles to bring out this finding. There are many lines that criss-cross. The conclusion is primarily based on the two thick lines - which show the average performance with and without fans of white and non-white players. The blue line (non-white) inched to the right (better performance) while the red line (white) shifted slightly to the left.

If the reader wants to understand the chart fully, there's a lot to take in. All (presumably) players are ranked by the performance score from lowest to highest into ten equally sized tiers (known as "deciles"). They are sorted by the 2019 performance when fans were in the stadiums. Each tier is represented by the average performance score of its members. These are the values shown on the top axis labeled "with fans".

Then, with the tiers fixed, the players are rated in 2020 when stadiums were empty. For each tier, an average 2020 performance score is computed, and compared to the 2019 performance score.

The following chart reveals the structure of the data:


The players are lined up from left to right, from the worst performers to the best. Each decile is one tenth of the players, and is represented by the average score within the tier. The vertical axis is the actual score while the horizontal axis is a relative ranking - so we expect a positive correlation.

The blue line shows the 2019 (with fans) data, which are used to determine tier membership. The gray dotted line is the 2020 (no fans) data - because they don't decide the ranking, it's possible that the average score of a lower tier (e.g. tier 3 for non-whites) is higher than the average score of a higher tier (e.g. tier 4 for non-whites).

What do we learn from the graphic?

It's very hard to know if the blue and gray lines are different by chance or by whether fans were in the stadium. The maximum gap between the lines is not quite 0.2 on the raw score scale, which is roughly a one-decile shift. It'd be interesting to know the variability of the score of a given player across say 5 seasons prior to 2019. I suspect it could be more than 0.2. In any case, the tiny shifts in the averages (around 0.05) can't be distinguished from noise.


This type of analysis is tough to do. Like other observational studies, there are multiple problems of biases and confounding. Fan attendance was not the only thing that changed between 2019 and 2020. The score used to rank players is a "Fantacalcio algorithmic match-level fantasy-football score." It's odd that real-life players should be judged by their fantasy scores rather than their on-the-field performance.

The causal model appears to assume that every non-white player gets racially abused. At least, the analyst didn't look at the curves above and conclude, post-hoc, that players in the third decile are most affected by racial abuse - which is exactly what has happened with the observational studies I have featured on the book blog recently.

Being a Serie A fan, I happen to know non-white players are a small minority so the error bars are wider, which is another issue to think about. I wonder if this factor by itself explains the shifts in those curves. The curve for white players has a much higher sample size thus season-to-season fluctuations are much smaller (regardless of fans or no fans).





Did the pandemic drive mass migration?

The Wall Street Journal ran this nice compact piece about migration patterns during the pandemic in the U.S. (link to article)


I'd look at the chart on the right first. It shows the greatest net flow of people out of the Northeast to the South. This sankey diagram is nicely done. The designer shows restraint in not printing the entire dataset on the chart. If a reader really cares about the net migration from one region to a specific other region, it's easy to estimate the number even though it's not printed.

The maps succinctly provide readers the definition of the regions.

To keep things in perspective, we are talking around 100,000 when the death toll of Covid-19 is nearing 600,000. Some people have moved but almost everyone else haven't.


The chart on the left breaks down the data in a different way - by urbanicity. This is a variant of the stacked column chart. It is a chart form that fits the particular instance of the dataset. It works only because in every month of the last three years, there was a net outflow from "large metro cores". Thus, the entire series for large metro cores can be pointed downwards.

The fact that this design is sensitive to the dataset is revealed in the footnote, which said that the May 2018 data for "small/medium metro" was omitted from the chart. Why didn't they plot that number?

It's the one datum that sticks out like a sore thumb. It's the only negative number in the entire dataset that is not associated with "large metro cores". I suppose they could have inserted a tiny medium green slither in the bottom half of that chart for May 2018. I don't think it hurts the interpretation of the chart. Maybe the designer thinks it might draw unnecessary attention to one data point that really doesn't warrant it.


See my collection of posts about Wall Street Journal graphics.

These are the top posts of 2020

It's always very interesting as a writer to look back at a year's of posts and find out which ones were most popular with my readers.

Here are the top posts on Junk Charts from 2020:

How to read this chart about coronavirus risk

This post about a New York Times scatter plot dates from February, a time when many Americans were debating whether Covid-19 was just the flu.

Proportions and rates: we are no dupes

This post about a ArsTechnica chart on the effects of Covid-19 by age is an example of designing the visual to reflect the structure of the data.

When the pie chart is more complex than the data

This post shows a 3D pie chart which is worse than a 2D pie chart.

Twitter people upset with that Covid symptoms diagram

This post discusses some complicated graphics designed to illustrate complicated datasets on Covid-19 symptoms.

Cornell must remove the logs before it reopens in the fall

This post is another warning to think twice before you use log scales.

What is the price of objectivity?

This post turns an "objective" data visualization into a piece of visual story-telling.

The snake pit chart is the best election graphic ever

This post introduces my favorite U.S. presidential election graphic, designed by the FiveThirtyEight team.


Here is a list of posts that deserve more attention:

Locating the political center

An example of bringing readers as close to the insights as possible

Visualizing change over time

An example of designing data visualization to reflect the structure of multivariate data

Bloomberg made me digest these graphics slowly

An example of simple and thoughtful graphics

The hidden bad assumption behind most dual-axis time-series charts

Read this before you make a dual-axis chart

Pie chart conventions

Read this before you make a pie chart

Looking forward to bring you more content in 2021!

Happy new year.

Is this an example of good or bad dataviz?

This chart is giving me feelings:


I first saw it on TV and then a reader submitted it.

Let's apply a Trifecta Checkup to the chart.

Starting at the Q corner, I can say the question it's addressing is clear and relevant. It's the relationship between Trump and McConnell's re-election. The designer's intended message comes through strongly - the chart offers evidence that McConnell owes his re-election to Trump.

Visually, the graphic has elements of great story-telling. It presents a simple (others might say, simplistic) view of the data - just the poll results of McConnell vs McGrath at various times, and the election result. It then flags key events, drawing the reader's attention to those. These events are selected based on key points on the timeline.

The chart includes wise design choices, such as no gridlines, infusing the legend into the chart title, no decimals (except for last pair of numbers, the intention of which I'm not getting), and leading with the key message.

I can nitpick a few things. Get rid of the vertical axis. Also, expand the scale so that the difference between 51%-40% and 58%-38% becomes more apparent. Space the time points in proportion to the dates. The box at the bottom is a confusing afterthought that reduces rather than assists the messaging.

But the designer got the key things right. The above suggestions do not alter the reader's expereince that much. It's a nice piece of visual story-telling, and from what I can see, has made a strong impact with the audience it is intended to influence.

_trifectacheckup_junkchartsThis chart is proof why the Trifecta Checkup has three corners, plus linkages between them. If we just evaluate what the visual is conveying, this chart is clearly above average.


In the D corner, we ask: what the Data are saying?

This is where the chart runs into several problems. Let's focus on the last two sets of numbers: 51%-40% and 58%-38%. Just add those numbers and do you notice something?

The last poll sums to 91%. This means that up to 10% of the likely voters responded "not sure" or some other candidate. If these "shy" voters show up at the polls as predicted by the pollsters, and if they voted just like the not shy voters, then the election result would have been 56%-44%, not 51%-40%. So, the 58%-38% result is within the margin of error of these polls. (If the "shy" voters break for McConnell in a 75%-25% split, then he gets 58% of the total votes.)

So, the data behind the line chart aren't suggesting that the election outcome is anomalous. This presents a problem with the Q-D and D-V green arrows as these pairs are not in sync.


In the D corner, we should consider the totality of the data available to the designer, not just what the designer chooses to utilize. The pivot of the chart is the flag annotating the "Trump robocall."

Here are some questions I'd ask the designer:

What else happened on October 31 in Kentucky?

What else happened on October 31, elsewhere in the country?

Was Trump featured in any other robocalls during the period portrayed?

How many robocalls were made by the campaign, and what other celebrities were featured?

Did any other campaign event or effort happen between the Trump robocall and election day?

Is there evidence that nothing else that happened after the robocall produced any value?

The chart commits the XYopia (i.e. X-Y myopia) fallacy of causal analysis. When the data analyst presents one cause and one effect, we are cued to think the cause explains the effect but in every scenario that is not a designed experiment, there are multiple causes at play. Sometimes, the more influential cause isn't the one shown in the chart.


Finally, let's draw out the connection between the last set of poll numbers and the election results. This shows why causal inference in observational data is such a beast.

Poll numbers are about a small number of people (500-1,000 in the case of Kentucky polls) who respond to polling. Election results are based on voters (> 2 million). An assumption made by the designer is that these polls are properly conducted, and their results are credible.

The chart above makes the claim that Trump's robocall gave McConnell 7% more votes than expected. This implies the robocall influenced at least 140,000 voters. Each such voter must fit the following criteria:

  • Was targeted by the Trump robocall
  • Was reached by the Trump robocall (phone was on, etc.)
  • Responded to the Trump robocall, by either picking up the phone or listening to the voice recording or dialing a call-back number
  • Did not previously intend to vote for McConnell
  • If reached by a pollster, would refuse to respond, or say not sure, or voting for McGrath or a third candidate
  • Had no other reason to change his/her behavior

Just take the first bullet for example. If we found a voter who switched to McConnell after October 31, and if this person was not on the robocall list, then this voter contributes to the unexpected gain in McConnell votes but weakens the case that the robocall influenced the election.

As analysts, our job is to find data to investigate all of the above. Some of these are easier to investigate. The campaign knows, for example, how many people were on the target list, and how many listened to the voice recording.





Podcast highlights

Recently, I made a podcast for Ryan Ray, which you can access here. The link sends you to a 14-day free trial to his newsletter, which is where he publishes his podcasts.


Ryan contacted me after he read my book Numbers Rule Your World (link). I was happy to learn that he enjoyed the stories, and during the podcast, he gave an example of how he applied the statistical concepts to other situations.

During the podcast, you will hear:

  • I have a line in my course syllabus that reads "after you take this class, you will not be able to look at numbers (in the media) with a straight face ever again." That's a goal of mine. And it also applies to my books.

  • Why are most statisticians skeptics

  • Figuring out the statistical conclusions is the easy part while the hardest challenge is to find a way to communicate them to a non-technical audience. I went through many drafts before I landed on the precise language used in those stories.

  • Why "correlation is not causation" is not useful practical advice
  • You can't unsee something you've already seen, and this creates hindsight bias
  • The biggest bang for the buck when improving statistical models is improving data quality

  • Some models, such as polls and election forecasts, can be thought of as thermometers measuring the mood of the respondents at the time of polling.


To hear the podcast, visit Ryan Ray's website.

A testing mess: one chart, four numbers, four colors, three titles, wrong units, wrong lengths, wrong data

Twitterstan wanted to vote the following infographic off the island:


(The publisher's website is here but I can't find a direct link to this graphic.)

The mishap is particularly galling given the controversy swirling around this year's A-Level results in the U.K. For U.S. readers, you can think of A-Levels as SAT Subject Tests, which in the U.K. are required of all university applicants, and represent the most important, if not the sole, determinant of admissions decisions. Please see the upcoming post on my book blog for coverage of the brouhaha surrounding the statistical adjustments (to be posted sometime this week, it's here.).

The first issue you may notice about the chart is that the bar lengths have no relationship with the numbers printed on them. Here is a scatter plot correlating the bar lengths and the data.


As you can see, nothing.

Then, you may wonder what the numbers mean. The annotation at the bottom right says "Average number of A level qualifications per student". Wow, the British (in this case, English) education system is a genius factory - with the average student mastering close to three thousand subjects in secondary (high) school!

TES is the cool name for what used to be the Times Educational Supplement. I traced the data back to Ofqual, which is the British regulator for these examinations. This is the Ofqual version of the above chart:


The data match. You may see that the header of the data table reads "Number of students in England getting 3 x A*". This is a completely different metric than number of qualifications - in fact, this metric measures geniuses. "A*" is the U.K. equivalent of "A+". When I studied under the British system, there was no such grade. I guess grade inflation is happening all over the world. What used to be A is now A+, and what used to be B is now A. Scoring three A*s is tops - I wonder if this should say 3 or more because I recall that you can take as many subjects as you desire but most students max out at three (may have been four).

The number of students attaining the highest achievement has increased in the last two years compared to the two years before. We can't interpret these data unless we know if the number of students also grew at similar rates.

The units are students while the units we expect from the TES graphic should be subjects. The cutoff for the data defines top students while the TES graphic should connote minimum qualification, i.e. a passing grade.

Now, the next section of the Ofqual infographic resolves the mystery. Here is the chart:


This dataset has the right units and measurement. There is almost no meaningful shift in the last four years. The average number of qualifications per student is only different at the second decimal place. Replacing the original data with this set removes the confusion.


While I was re-making this chart, I also cleaned out the headers and sub-headers. This is an example of software hegemony: the designer wouldn't have repeated the same information three times on a chart with four numbers if s/he wasn't prompted by software defaults.


The corrected chart violates one of the conventions I described in my tutorial for DataJournalism.com: color difference should reflect data difference.

In the following side-by-side comparison, you see that the use of multiple colors on the left chart signals different data - note especially the top and bottom bars which carry the same number, but our expectation is frustrated.



[P.S. 8/25/2020. Dan V. pointed out another problem with these bar charts: the bars were truncated so that the bar lengths are not proportional to the data. The corrected chart is shown on the right below:


8/26/2020: added link to the related post on my book blog.]

Consumption patterns during the pandemic

The impact of Covid-19 on the economy is sharp and sudden, which makes for some dramatic data visualization. I enjoy reading the set of charts showing consumer spending in different categories in the U.S., courtesy of Visual Capitalist.

The designer did a nice job cleaning up the data and building a sequential story line. The spending are grouped by categories such as restaurants and travel, and then sub-categories such as fast food and fine dining.

Spending is presented as year-on-year change, smoothed.

Here is the chart for the General Commerce category:


The visual design is clean and efficient. Even too sparse because one has to keep returning to the top to decipher the key events labelled 1, 2, 3, 4. Also, to find out that the percentages express year-on-year change, the reader must scroll to the bottom, and locate a footnote.

As you move down the page, you will surely make a stop at the Food Delivery category, noting that the routine is broken.


I've featured this device - an element of surprise - before. Remember this Quartz chart that depicts drinking around the world (link).

The rule for small multiples is to keep the visual design identical but vary the data from chart to chart. Here, the exceptional data force the vertical axis to extend tremendously.

This chart contains a slight oversight - the red line should be labeled "Takeout" because food delivery is the label for the larger category.

Another surprise is in store for us in the Travel category.


I kept staring at the Cruise line, and how it kept dipping below -100 percent. That seems impossible mathematically - unless these cardholders are receiving more refunds than are making new bookings. Not only must the entire sum of 2019 bookings be wiped out, but the records must also show credits issued to these credit (or debit) cards. It's curious that the same situation did not befall the airlines. I think many readers would have liked to see some text discussing this pattern.


Now, let me put on a data analyst's hat, and describe some thoughts that raced through my head as I read these charts.

Data analysis is hard, especially if you want to convey the meaning of the data.

The charts clearly illustrate the trends but what do the data reveal? The designer adds commentary on each chart. But most of these comments count as "story time." They contain speculation on what might be causing the trend but there isn't additional data or analyses to support the storyline. In the General Commerce category, the 50 to 100 percent jump in all subcategories around late March is attributed to people stockpiling "non-perishable food, hand sanitizer, and toilet paper". That might be true but this interpretation isn't supported by credit or debit card data because those companies do not have details about what consumers purchased, only the total amount charged to the cards. It's a lot more work to solidify these conclusions.

A lot of data do not mean complete or unbiased data.

The data platform provided data on 5 million consumers. We don't know if these 5 million consumers are representative of the 300+ million people in the U.S. Some basic demographic or geographic analysis can help establish the validity. Strictly speaking, I think they have data on 5 million card accounts, not unique individuals. Most Americans use more than one credit or debit cards. It's not likely the data vendor have a full picture of an individual's or a family's spending.

It's also unclear how much of consumer spending is captured in this dataset. Credit and debit cards are only one form of payment.

Data quality tends to get worse.

One thing that drives data analyst nuts. The spending categories are becoming blurrier. In the last decade or so, big business has come to dominate the American economy. Big business, with bipartisan support, has grown by (a) absorbing little guys, and (b) eliminating boundaries between industry sectors. Around me, there is a Walgreens, several Duane Reades, and a RiteAid. They currently have the same owner, and increasingly offer the same selection. In the meantime, Walmart (big box), CVS (pharmacy), Costco (wholesale), etc. all won regulatory relief to carry groceries, fresh foods, toiletries, etc. So, while CVS or Walgreens is classified as a pharmacy, it's not clear that what proportion of the spending there is for medicines. As big business grows, these categories become less and less meaningful.

Hope and reality in one Georgia chart

Over the weekend, Georgia's State Health Department agitated a lot of people when it published the following chart:


(This might have appeared a week ago as the last date on the chart is May 9 and the title refers to "past 15 days".)

They could have avoided the embarrassment if they had read my article at DataJournalism.com (link). In that article, I lay out a set of the "unspoken conventions," things that visual designers are, or should be, doing more or less in their sleep. Under the section titled "Order", I explain the following two "rules":

  • Place values in the natural order when it is available
  • Retain the same order across all plots in a panel of charts

In the chart above, the natural order for the horizontal (time) axis is time running left to right. The order chosen by the designer  is roughly but not precisely decreasing height of the tallest column in each daily group. Many observers suggested that the columns were arranged to give the appearance of cases dropping over time.

Within each day, the counties are ordered in decreasing number of new cases. The title of the chart reads "number of cases over time" which sounds like cumulative cases but it's not. The "lead" changed hands so many times over the 15 days, meaning the data sequence was extremely noisy, which would be unlikely for cumulative cases. There are thousands of cases in each of these counties by May. Switching the order of the columns within each daily group defeats the purpose of placing these groups side-by-side.

Responding to the bad press, the department changed the chart design for this week's version:


This chart now conforms to the two spoken rules described above. The time axis runs left to right, and within each group of columns, the order of the counties is maintained.

The chart is still very noisy, with no apparent message.


Next, I'd like to draw your attention to a Data issue. Notice that the 15-day window has shifted. This revised chart runs from May 2 to May 16, which is this past Saturday. The previous chart ran from Apr 26 to May 9. 

Here's the data for May 8 and 9 placed side by side.


There is a clear time lag of reporting cases in the State of Georgia. This chart should always exclude the last few days. The case counts keep going up until it stabilizes. The same mistake occurs in the revised chart - the last two days appear as if new cases have dwindled toward zero when in fact, it reflects a lag in reporting.

The disconnect between the Question being posed and the quality of the Data available dooms this visualization. It is not possible to provide a reliable assessment of the "past 15 days" when during perhaps half of that period, the cases are under-counted.


Nyt_tryingtobefashionableThis graphical distortion due to "immature" data has become very commonplace in Covid-19 graphics. It's similar to placing partial-year data next to full-year results, without calling out the partial data.

The following post from the ancient past (2005!) about a New York Times graphic shows that calling out this data problem does not actually solve it. It's a less-bad kind of thing.

The coronavirus data present more headaches for graphic designers than the financial statistics. Because of accounting regulations, we know that only the current quarter's data are immature. For Covid-19 reporting, the numbers are being adjusted for days and weeks.

Practically all immature counts are under-estimates. Over time, more cases are reported. Thus, any plots over time - if unadjusted - paint a misleading picture of declining counts. The effect of the reporting lag is predictable, having a larger impact as we run from left to right in time. Thus, even if the most recent data show a downward trend, it can eventually mean anything: down, flat or up. This is not random noise though - we know for certain of the downward bias; we just don't know the magnitude of the distortion for a while.

Another issue that concerns coronavirus reporting but not financial reporting is inconsistent standards across counties. Within a business, if one were to break out statistics by county, the analysts would naturally apply the same counting rules. For Covid-19 data, each county follows its own set of rules, not just  how to count things but also how to conduct testing, and so on.

Finally, with the politics of re-opening, I find it hard to trust the data. Reported cases are human-driven data - by changing the number of tests, by testing different mixes of people, by delaying reporting, by timing the revision of older data, by explicit manipulation, ...., the numbers can be tortured into any shape. That's why it is extremely important that the bean-counters are civil servants, and that politicians are kept away. In the current political environment, that separation between politics and statistics has been breached.


Why do we have low-quality data? Human decisions, frequently political decisions, adulterate the data. Epidemiologists are then forced to use the bad data, because that's what they have. Bad data lead to bad predictions and bad decisions, or if the scientists account for the low quality, predictions with high levels of uncertainty. Then, the politicians complain that predictions are wrong, or too wide-ranging to be useful. If they really cared about those predictions, they could start by being more transparent about reporting and more proactive at discovering and removing bad accounting practices. The fact that they aren't focused on improving the data gives the game away. Here's a recent post on the politics of data.