Do wind turbines kill all the birds? Or is it the cats instead?

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Donald J. Trump often cites a very questionable number. Then he shows a lack of credibility by continuing to repeat it - even after it has been debunked. A number also needs to be put into context. How does it compare with other related numbers?

 

Back on June 2, 2016, there is an article by Vanessa Schipani at FactCheck.org titled Trump’s Hot Air on Wind Energy which said that:

 

“Scientific studies have come up with a wide range of mean estimates concerning bird mortality due to wind farms – from 20,000 to 573,000 bird deaths per year. But no study has estimated that wind farms presently kill 1 million birds a year, as Trump claimed.

 

 …A 2013 Nature study by Loss and others estimated that ‘free-ranging domestic cats kill 1.3–4.0 billion birds’ in the U.S. annually. But the researchers added, ‘Un-owned cats, as opposed to owned pets, cause the majority of this mortality.’

 In another study published in the journal The Condor in 2014, Loss and colleagues also estimated ‘that between 365 [million] and 988 million birds … are killed annually by building collisions in the U.S., with roughly 56% of mortality at low-rises, 44% at residences, and <1% at high-rises.’”

There is another article by Sophie Lewis at CBS News on October 23, 2020 titled Trump claims wind energy “kills all the birds.” Cats and windows are actually much more to blame. She said:

 

“During Thursday night's final presidential debate between the president and former Vice President Joe Biden, Mr. Trump reiterated his stance against wind energy with a peculiar claim: It ‘kills all the birds.’ 

 ‘I know more about wind than you do,’ Mr. Trump told Biden when moderator Kristen Welker turned to the topic of climate change. ‘It's extremely expensive. Kills all the birds. It's very intermittent. Got a lot of problems.’ "

 

 

 

 

 

 

  

 

 

 

 

A more recent article by Hannah Ritchie in By the Numbers on January 30, 2024 titled How many birds do wind farms kill? had the statistics that are shown above in a horizontal bar chart with a logarithmic scale. Cats killed 2400 million birds, while wind turbines killed just 1.2 million. So cats killed 2000 times as many as wind turbines! And cats were one of six categories that killed more birds than wind turbines did.

 

There is also an article by Joe Schwarcz at the McGill Office for Science and Society on October 11, 2024 titled The Windmills in Donald Trump’s Mind. And another article by Michael Doyle at E&E News on September 12, 2025 is titled Wind turbines aren’t the biggest threat to birds.

 

An image of wind turbines in Power County, Idaho was cropped from this one at Wikimedia Commons.  

 

 

Displaying risks from vaccines and other dangers

 

 

 

 

 

 

 

 

 

 

 

 

There is an interesting article from Edzard Ernst on July 16, 2025 titled Yes, vaccination risks are real – but how do they compare with other dangers? In order to show the range from the tiny one in a hundred million for the MMR vaccine to one in 1,100 for drowning (and up to one) we need to use a chart with a logarithmic scale - as is shown above. I had discussed risk in a post on November 12, 2024 titled I am not going to throw out my black plastic spatulas.

 

Also, back on January 13, 2010 I had blogged about How thin is “extremely thin”? and used a table with a powers of ten for comparing thicknesses over a range in centimeters from one to one in ten to the minus eighth power.

 

Two very useful articles in the August 2025 issue of Toastmaster magazine on Five Tips for Depicting Data and Making Your Data Presentations Come to Life

 

 

 

 

 

 

 

 

 

The August 2025 issue of Toastmaster magazine has two very useful articles. One by Florian Bay on pages 20 to 23 is titled 5 Tips for Depicting Data and subtitled How to use numbers and graphs to create a compelling story. His five headings are:

 

Be Clear on Your Purpose

Select the Right Visual

Remove the Clutter

Engage with Colors but Don’t Create Rainbows

Use Text to Guide Your Viewers

 

The first two line and column bar charts, at the bottom of Page 20 are missing a label for the vertical axis, which should be Number of UNESCO Sites. As shown above, I have added it in red.

 

Regarding pie charts, he says:

 

“Visualizing proportions? Then use a pie chart, but only if you’ve got two or three slices to share; any more than that and the proportions get hard to interpret. Stacked bar charts can also be useful.”

 

I don’t think pie charts ever are useful, as I discussed in a blog post on September 16, 2008 titled Pie charts do not speak clearly; they just mumble and one on July 29, 2022 titled Is there any excuse for using a pie chart?

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

And his example with Yearly Passengers in London Heathrow Airport 2005-2024 (shown above) has the numbers of years shown vertically rather than horizontally. My Excel version omits the bullseyes around the pandemic data and just shows numbers for all those data points. (On July 4, 2017 I blogged about Why is your audience tilting their heads sideways?)

 

The second article by Charlene Phua following on pages 24 and 25 is titled Make Your Data Presentations Come to Life and subtitled Avoid these 4 missteps to ensure your audience stays engaged. Her four headings are:

 

Including data that isn’t relevant to topic or audience

Having an inaccurate gauge of your audience’s understanding

Providing data with no context or comparison

Neglecting other aspects of your performance

 

 

 

 

 

 

 

 

 

 

 

 

 

 

For the third she uses the example of annual global carbon dioxide emissions. She didn’t mention a number, but in 2022 they are 38,521,997,860 tons. We can ask What’s in it for me? The global Population is  8,021,407,192 people so it is 4.802 tons per capita, or 9605 pounds. Per day it is 26.3 pounds, and converting that to a cube of dry ice, it is one 7.75 inches on a side, as shown above.

 

On December 23, 2024 I blogged about How should we present a huge number like the two billion dollars earned by the Taylor Swift Eras tour? And on July 12, 2016 I blogged about How to make statistics understandable. Also, on August 17, 2011 I posted on How to make a large number incomprehensible or comprehensible and on July 15, 2011 I blogged on What can we say about a really big hole in the ground?

 

 

A visit from the mythical Bar Chart Police could have improved the 2025 book Speak, Memorably: the art of captivating an audience

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I have been reading a 2025 book by Bill McGowan and Juliana Silva which is titled Speak, Memorably: the art of captivating an audience. It’s not awful, but it’s not great either. There is no index. On August 2, 2025 I posted about A bogus car story from the 2025 book Speak, Memorably.

 

On page 10 of that book they state that:

 

“….in the chapters that follow, we commissioned a survey by the Clarity Media Group of over one hundred people. The respondent pool was comprised of full and part-time employees between the ages of twenty-five and sixty-five.”

 

There are 31 horizontal bar charts shown in this book on pages 11, 15,21, 27, 36, 38, 55, 58,60, 71, 113, 114, 135, 139, 153, 155, 168, 183, two on 185, 194, 197, 199, 204, 214, 215, 226, 228, 229, 243, and 244. They are shown in monochrome, and only are 3.3” wide by 1.5” high. The percent is labeled at the center of the bar, and the description of the question is to the right of the bar.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

My slightly different Excel version of the chart on page 11 for Do You Consider Your Public Speaking Skills at Work Important? is shown above. But those bars are not presented in what should be their order, with the most significant and largest percentage placed at the top. My revised version also is shown above.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Another example from page 168 about How Do You Determine What Your Audience Wants to Know? and a corrected version are shown above.

 

18 of 31 charts have this incorrect order, those on pages 11, 21, 27, 36, 113, 139, 153, 155, 168, 183, 185 bottom, 194, 199, 204, 214, 215, 228, and 229.

 

 

 

 

 

 

 

 

 

 

 

 

And as shown above, the chart on page 21 for How Often Do You Know with Certainty the First and Last Sentence of Each Slide? has silly labels from top to bottom of: Never, Rarely, Sometimes, and Frequently. That is opposite to what should be: Frequently, Sometimes Rarely, Never. They used the same sequence on pages 27, 113, 139, 155, 199, and 215. Also the chart on page 194 has Never, Seldom, Occasionally, Frequently.

 

Back on September 6, 2022 I blogged that we should Beware of surveys with small sample sizes, which have large margins of error. There is a Wikipedia page about Margin of error. For a typical national survey with a sample size of 1,000 the margin of error is 3.1%. For a sample size of 100 the margin of error instead  is 9.8% (as is shown above in my revised version for page 11).

 

Is the difference between the largest percentage and the second largest percentage twice the margin of error (or 20%)? That only is true for ten charts, those on pages 36, 55, 58, 71, 135, 139, 168, 183, 185 top, and 215. Not the other 21 charts!

 

I first mentioned the mythical Bar Chart Police (and showed a version of their badge) in a post on November 3, 2023 titled Five of six British age groups are more confident about public speaking after COVID.

 

There is more than one answer to a question like when a season starts

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Recently my wife showed me that there is a Reddit community (subreddit) called Data Is Beautiful. Over there I found a reference to an article by Jasmine Mithani and Kaleigh Rogers at FiveThirtyEight on November 24, 2021 titled Our very unscientific poll on when each season starts. I would use the astronomical definitions with equinox or solstice, but, as shown above for winter via a PowerPoint donut chart, there are other possibilities like holidays.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I dislike donut charts (because it’s hard to discern small differences), so I have shown results for all seasons above via a single bar chart. For fall, the autumnal equinox is the most common answer, but for the other three seasons the astronomical definition comes in second or third. Also 1% even say fall starts when pumpkin spice lattes are available again.  

 

Are Americans really more afraid of clowns than climate change, terrorism, and death?

That question really should be are more Americans afraid of, and then the answer really is no on those first two. Vox and Morning Consult did a poll of 1999 U.S. adults on October 15 to 17, 2016. They asked How Afraid of clowns are you? Their four possible answers were Very Afraid (8%), Somewhat Afraid (14%), A Little Afraid (20%), or Not Afraid At All (53%). (5% had No Opinion). Results were reported in an article by Zachary Crockett at Vox on October 21, 2016 titled Americans are more afraid of clowns than climate change, terrorism, and ... death.
 
He originally had said:

“We compared the results of our poll with a poll recently conducted by Chapman University, which asked 1,511 Americans to identify their greatest fears from a list of topics. Clowns outranked every single fear, save for ‘government corruption.’ (Note that the Chapman poll only included ‘very afraid’ and ‘afraid’ as voting options, while our poll had ‘very afraid,’ ‘somewhat afraid,’ and ‘a little afraid.’)”

After I emailed him and pointed him to my October 14th blog post titled In the 2016 Chapman Survey of American Fears public speaking was ranked 33rd out of 79 fears, he changed the sentence in parentheses to instead read:

“(Note that our poll and the Chapman poll had slightly different answer options: Chapman’s had ‘very afraid,’ “afraid,” and ‘a little afraid,’ while our poll had ‘very afraid,’ ‘somewhat afraid,’ and ‘a little afraid.’)”

Results for clowns in those two surveys were as follows:

Zachary originally showed a stacked bar chart with those detailed results (including five age groups) but later deleted it.

But that chart still exists as an image at Twitter, as is shown above.

There also was a bogus bar chart that compared the results for the sum of the first three answers (42%) from the Vox survey with results from the 2016 Chapman Survey of American Fears which had listed the sum for just the top two answers - Very Afraid and Afraid. It also was deleted, but a version still exists at Twitter, as shown below.

I’ve also shown a corrected version of that now deleted second bar chart, with his answer changed to the correct 22%. But the text still makes misleading comparisons using 42%:

“Americans — at least, those surveyed in our poll — are more afraid of clowns than a possible terrorist attack, a family member dying, climate change, biological warfare, and the always-terrifying Obamacare.”
 
The Vox survey had more details and other questions too. One was Should the following groups do more, less, or about the same to stop clowns from scaring people in public places? Another asked how creepy Americans found nine different images of clowns.

The image of five clowns came from the Library of Congress.

UPDATE

At some point Zachary put those bar charts back into his article. Once again you can see how bogus the comparison is.

Unfortunately the Vox survey has repeatedly been quoted on the Web. On November 7th at Newsmax Larry Bell had an article that asked Do People Fear Clowns Over Climate Change? The answer still is no. Larry is a professor at the University of Houston, so I'd have expected him to catch a bogus statistic.

According to the 2016 Chapman Survey of American Fears, adults are less than Afraid of Corrupt (federal) Government Officials and only Slightly Afraid of the Affordable Health Care Act (aka Obamacare)

In the 2016 Chapman Survey on American Fears 1,511 adults were asked 79 questions that began with:

“How afraid are you of the following?”

They were asked to choose from one of four answers (fear levels):

1] Not Afraid

2] Slightly Afraid

3] Afraid

4] Very Afraid

In the Chapman blog post about the survey on October 11th titled America’s Top Fears 2016 there was a Complete List of Fears ranked by the sum of the percentages for Afraid and Very Afraid (and also shown alphabetically). The most common fear was Corrupt (federal) government officials (60.6%). But the press release on that same day was incorrectly titled What Do Americans Fear Most? Chapman University’s Third Annual Survey of American Fears Released. It instead should have said What Do the Most Americans Fear? Many others like USA Today repeated that incorrect claim - their article was titled Survey reveals what Americans fear the most.



HOW AFRAID ARE AMERICANS OF CORRUPTION AND OTHER THINGS?

Where was that fear score for Corrupt government officials? Was it way up at 3.75, enough to make us Scream like that man in the famous painting by Edward Munch (shown above). Or was it 3.5, or 3.25, or just 3.0 (Afraid). A fear score can be calculated from the answers for each question in the Detailed Results. The formula simply is a weighted average of the proportions:

Fear Score = [ 1x(% for Not Afraid) +  2x(% for Slightly Afraid)
 + 3x(% for Afraid) + 4x(% for Very Afraid)]/100  

I discussed Fear Scores in an October 30, 2015 blog post titled According to the 2015 Chapman Survey of American Fears, adults are less than Afraid of federal Government Corruption and only Slightly Afraid of Public Speaking.

The Fear Score for Corrupt government officials was just 2.678, or not even Afraid (3.0).


TOP 40 AND BOTTOM 40 LISTS OF FEAR SCORES

I calculated all the the Fear Scores. Two bar charts show results for the TOP 40 and the BOTTOM 40. Click on them to see a larger, clearer view. The highest score, for Corrupt government officials, is just 2.678, which isn’t even at Afraid. Often the rankings based on Fear Score and  the Sum of [Very Afraid + Afraid] percentages are similar.

For The Affordable Care Act (Obamacare), which was ranked tenth based on the Sum of [Very Afraid + Afraid] percentages, the Fear Score is 2.001 - or just Slightly Afraid. The rank based on a Fear Score is just 25th.

For Public speaking the Fear Score is 1.933, or not even Slightly Afraid and was ranked the 33rd highest. The very lowest Fear Score for Clowns is only 1.26.





WHAT WAS THE RANGE FOR THOSE FEAR SCORES?

As is shown above in a line graph, those fear scores cover a range from a low of 1.26 to a high of 2.68. Data cover just under half the total range of 3.00 you might expect to find (and only cover the lower part of that range). The highest score is not very frightening at all. It reminded me of a 1965 song by The Radiants - (It) Ain’t No Big Thing.


PEOPLE I LOVE DYING OR BECOMING SERIOUSLY ILL WERE NOT ON LAST YEAR’S LIST OF QUESTIONS. IS THIS SOMETHING NEW FROM CHAPMAN U? 

No, it instead is borrowed from something very old. Back in 1965 James H. Geer had a magazine article published on The Development of a Scale to Measure Fear. He found that for both male and female university students the greatest fear was Death of a Loved One. For  females, Illness or Injury to Loved Ones was second, and for males it was third. Speaking Before a Group was sixth for males and seventh for females. See my October 10, 2012 blog post titled In a 1965 study of university students, fear of public speaking ranked sixth for men and seventh for women.

Not telling the truth with charts

Yesterday Seth Godin blogged about Telling the truth with charts, but he didn’t really. He began with the second chart from an article by Jordan Weissmann on The Decline of the American Book Lover  in The Atlantic last January. He whined that it presented color coded data from three years (Gallup  1978 and 1990, and Pew 2014) shown in reverse chronological order, and five sets of percentages for number of books read (none, 1 to 5, 6 to 10, 11 or more, and no answer). Seth claimed the 1990 Gallup poll told us nothing, omitted it, and also dropped the no answer category. Then he left out the 1 to 5, and 6 to 10 books read categories. What was left is a comparison of 1978 and 2014 data for number of books read (none, or 11 or more). He showed the following chart, which has serious problems of both form and content:

The form problem is his use of red and green colors, which I have called Christmas Camouflage because some color blind people will see both as olive drab, as shown above in the Vischeck Deuteranope Simulation view at the right.

The content problem is a change of scale between the results for zero books and heavy readers (more than 11 books). For zero books the left bar represents 8%, while for heavy readers the right bar with the same height represents 28%. Also, the left chart runs downward from zero while the right chart runs upward from a different origin. For heavy readers the left bar (for 42%) should be 1.5 times the height for 28% but instead is 2.5 times that height. An honest chart with the same scale and origin reveals that there still is a higher percentage of heavy readers (28%) than non-readers (23%) and it looks like this:

Does the 1990 Gallup Poll tell us nothing? No, it tells us something useful - that the percentages were changing from those the measured in 1978, and so the difference for 2014 is not from Pew and Gallup just phrasing their questions differently. Here is another honest chart including that data:

Michele Bachmann pinned my bogometer!

In her Tea Party Express reply to the State of the Union address Representative Michele Bachmann showed us an Unemployment Rate bar chart. It supposedly compares the rate each year under the Bush and Obama administrations. The implied message obviously was that the recession and resulting misery was solely due to Obama. But, wait a minute. Why are there three blue bars for an administration that’s been in office for just two years? The opposite of credibility is bogosity. It’s measured with a bogometer, and “pinning my bogometer” means that you said something incredibly bogus.

 












The bar chart she used is shown above. (I have changed the background to purple for artistic reasons). When you see a bar chart with annual entries, they usually are the average for that year. The tiny legend at the bottom says that these data actually are just from the month of October (except for the bogus 2011 point from December 2010).  Going from 2008 to 2009 the rate jumped up by 3.5%. 

 











What was the monthly unemployment rate doing from 2008 to 2010? You can find the data and another very long bar chart on the Misery Index web site. A graph is shown above. Unemployment rose during 2008 under Bush, and it continued to rise in 2009 after Obama took over. Both Republicans and Democrats got us into this mess, and they will need to work together to get us out of it.

I don’t know why Bachmann chose to use monthly numbers from October in her chart. If she had chosen to use May or June the rate jump would have been 4.0% rather than 3.5%. (Using December it would have been a less impressive 2.6%).

THE WALL STREET JOURNAL Guide to Information Graphics

Last week I found this remarkable guide on the new books shelf at my public library. It is subtitled: The Dos and Don’ts of Presenting Data, Facts, and Figures. Dona M. Wong crammed it with clear, easy to read guidance for anyone who uses charts and graphs. You can preview a sample here.

If you use Excel or PowerPoint, then you need to read this book from cover to cover, which will not take long. This 157-page book contains about 500 graphics, and defines the phrase “profusely illustrated.” The list price is $30, but Amazon sells it for $20. Borrow it or buy it!

Dona spent the past nine years as the graphics director for the Wall Street Journal. Before that she was the graphics editor for the daily Business, Sunday Business, and Monday Media Business sections in the New York Times. She has an MFA from Yale on information design, and her thesis advisor was Edward Tufte.

The image shown above is based on the very first graphic from her introduction.

Advice on effectively communicating numbers

In 2004 Professor Stephen Few, who teaches in the MBA program at the University of California, Berkeley wrote a 280 page hardcover book called Show Me the Numbers: Designing Tables and Graphs to Enlighten. Books are wonderful, but reading them takes lots of time.

In 2005 he produced a more manageable 24 page whitepaper on Effectively Communicating Numbers. It discusses how to decide if you should use a table or a graph. Then it discusses choosing and using different types of graphs in considerable detail. The paper concludes with a single page appendix showing the steps involved in designing a graph. (This paper also contains a 10 page appendix by Proclarity Corporation in Boise which illustrates the use of their software. In Spring 2006 Proclarity was purchased by Microsoft and their software became the foundation for PerformancePoint Server.)

You can find most of Professor Few’s other articles in a library on his website. An exception is a recent 80 page presentation on Graph Design for Effective Communication from June 2008.

Both documents do an excellent job of covering the topic of displaying numerical data so that it produces “interocular traumatic impact”, which is his playful academic jargon for “hits you right between the eyes”.

Practical Rules for Using Color in Charts

In 2004 Stephen Few wrote a book called Show Me the Numbers: Designing Tables and Graphs to Enlighten. In a 13 page paper on Practical rules for using color in charts he discusses and illustrates nine rules of graphics, which are:

Rule 1: If you want different objects of the same color in a table or graph to look the same, make sure that the background (the color that surrounds them) is consistent. (A gradient background just adds confusion).

Rule 2: If you want objects in a table or graph to be easily seen, use a background color that contrasts sufficiently with the object.

Rule 3: Use color only when needed to serve a particular communication goal.

Rule 4: Use different colors only when they correspond to differences of meaning in the data. (Adding a different color for each country to a bar chart adds nothing when you already have identified their names).

Rule 5: Use soft, natural colors to display most information, and bright colors and/or dark colors to highlight information that requires greater attention. (He gives an example of palette with eight soft natural and bright highlight colors).

Rule 6: When using color to encode a sequential range of quantitative values, stick with a single hue (or a small set of closely related hues) and vary intensity from pale colors for low values to increasingly darker and brighter colors for high values.

Rule 7: Non-data components of tables and graphs should be displayed just visibly enough to perform their role, but not more so, for excessive salience could cause them to distract attention from the data. (The scales and borders should not visually overwhelm your data).

Rule 8: To guarantee that most people who are colorblind can distinguish groups of data that are color coded, avoid using a combination of red and green in the same display. (About 10% of men cannot tell red from green, and identify traffic lights by position only!)

Rule 9: Avoid using visual effects in graphs. (A plain bar chart is preferable to one with three-dimensional rods).