Until a few months ago, Nate Silver was a nerd hero. If you’ve never heard of him, perhaps you’ve heard of sabermetrics (or you’ve seen Moneyball, a movie about sabermetrics). Well, Silver is best known for applying sabermetric models to the study of politics, especially presidential races. The accuracy of Silver’s predictions for the 2008 and 2012 elections earned him accolades and fame.

Silver runs FiveThirtyEight.com, a blog about number crunching and prediction covering most aspects of life including sports and politics. The folks at FiveThirtyEight are basically odds makers and people tend to misunderstand what their products really mean. That’s what this post is about.

Before Tuesday’s election, Silver took quite a bit of criticism for his low certainty in a Clinton win relative to other prediction models. Focusing on one piece in particular that kicked up a lot of dust, HuffPo’s Ryan Grim actually accused Silver of “…Unskewing Polls — All Of Them — In Trump’s Direction”, injecting too much subjective bias into his model. Silver fought back, noting that the way his model weights polls is based on empirical data, but lots of people didn’t buy it.

Silver’s final model gave Clinton a 71.4% chance of winning, but when Grim published his criticism, the number was closer to 65%. This was in stark contrast to others, many of whom gave her odds in the 90s. The final prediction of the New York Times was 85% and the HuffPo model that Grim was so confident in put her at 98%. You might be thinking that Grim is feeling pretty low right now, but I’m not so sure since he hedged his criticisms with:

If [Silver is] right, though, it was just a good guess…

Well, one could argue that nobody was right (except Allan Lichtman, who correctly predicted Trump using only 13 ‘key’ factors, some of which are highly subjective). But that doesn’t mean that everybody was wrong, either, despite the prediction-bashing that I’ve been seeing in my Facebook feed since the election. In Grim’s criticism, he said some things that made me question if he understood the very thing he was writing about and those comments also provide some insight into why people are so upset with Silver post-election.

First, let’s talk very briefly about what these prediction models do. Now, keep in mind that I’m going to oversimplify the process a lot for several reasons, but the details are not particularly relevant to my points.

Most of these models use an aggregate of polls to come up with a prediction. Some, like Silver’s model, include other information (e.g., economic factors) and/or they weight each poll based on its past performance. Silver’s model is highly complex, including a measure of inter-dependency between states. It is in the weighting of polls that Grim’s problem with Silver’s model lies; he felt that Silver’s weights were too subjective and biased toward a Trump win. One Facebook comment I saw even suggested that 538 purposefully made the election look close in order to create anxiety that would keep people clicking on their site.

But here’s what people seem to misunderstand the most about this process. A 55% chance of winning doesn’t necessarily mean “a close race” in terms of votes. Yes, those things go hand-in-hand, but you need to separate them for a deeper understanding. Most people see that 65% probability and their brains translate that number into frequencies — they imagine 65% of people voting for Clinton, which is of course not what the numbers mean. But even if your brain corrects that part of it, you may not be able to shake the concept completely. More on that in a bit.

Most of the modern prediction models do not spit out a victor. The Huffington Post, for example, did not say “Clinton will win”. Well, okay, Grim did:

If you want to put your faith in the numbers, you can relax. She’s got this.

but the model’s output was “Clinton has a 98% chance of winning”. It’s not actually a prediction, but an estimate of the probability that an event will occur. Now, if you have ever read any of my notes on The Odds Must Be Crazy or listened to a segment of it on Skepticality, you know that low-odds events happen all the time. And, quite frankly, the nearly 30% that Silver gave Trump is not even low.

But my point about this is that it is not accurate to say that a prediction like this was “wrong”. I might use that word in the case of HuffPo, whose 98% confidence in Clinton was misplaced, but even then, they gave Trump a 2% chance. That Trump won doesn’t make their prediction “wrong”. They predicted a 2% chance of a Trump win and that’s what happened. That’s how odds work. And in our evaluation of those processes, we have to fight our human brains that want to put things into boxes in irrational ways. [EDIT: If you roll a die, you have only a one-in-six chance of rolling a 6. So if you roll it and a six comes up, will you now say that prediction was wrong? That wouldn’t make sense, right?]

Grim should have known better, but he repeatedly suggested that he didn’t understand the difference between providing odds and picking a winner. He also seemed to think that the election outcome would tell us whose models were “right” — he said so three times in his piece.

Here’s the thing: the outcome of the election doesn’t even tell us for sure which model(s) was better. There just isn’t enough data, which is ironically one of the reasons the models appear to have failed (again, they didn’t ‘fail’).

Yet people are cursing the models and there’s a well-known psychological phenomenon to explain why. Human beings are notoriously bad at understanding and applying information about probability and concepts of uncertainty.

In general, when people hear there is a 10% chance of rain they expect drizzle and when they hear there is a 85% chance of rain, they expect a downpour. But that’s not what the prediction means. An 85% chance of rain means it might not rain, but if I were you, I’d take an umbrella.

Silver’s model also changed as often as new information came in, usually many times during a single day, which does not help people to understand it. At this point, political prediction is more complex than weather prediction, and humans have a difficult time fully understanding that. For example, when asked:

If there is a 55% chance of rain on Saturday and a 75% chance of rain on Sunday, what is the chance that it will rain at some point during the weekend?

you might be tempted to average the two and say 65%. However, the probability of rain over the weekend cannot be less than the probability of rain on a given day, so it must be at least 75%. Or you might be tempted to add them together; there is a famous story of the weatherman who said, “There’s a 50% chance of rain on Saturday and a 50% chance on Sunday, so there’s a 100% chance it will rain this weekend. For those curious, the answer is actually ~88%.

The bottom line is that Nate Silver’s model spit out a 71% chance of a Clinton win. The fact that she did not win is not a failure of the model. It’s just what happened. He also said there was a 10.5% chance that she would win the popular vote but lose in the electoral college. That’s not an insignificant probability, so it’s not a ‘fluke’ that it happened.

I have also seen many people, both before and after the election, suggest that these models are not scientific. I disagree. What makes something a science — hell, what makes it good or bad science — is not its ability to uncover truths or predict outcomes. What makes it a science (and the difference between good and bad science) is the method used.

Now all of this said, there are good reasons that we should not bet on these models for presidential races. The biggest reason is that we simply do not have enough information.

Models are built using information about what has happened in the past. That is true for everything from baseball to weather. And we simply do not have enough real world data about presidential races because:

• The outcomes for presidential races are nearly dichotomous, at least the one measure that most people are interested in: who will win.
• We only have one trial/case every four years.
• The farther back in time we go, the less input info (e.g., polls and economic indicators). While some of this information can be filled in using various techniques such as bootstrapping, the less raw data we have, the less reliable the model will be.

These models will get better as time goes on, but with a race only every four years, it will be a very, very slow process of perfecting. And even then there will always be times in which the predicted underdog wins. Always. Because even a 1% chance is a chance.

But the absolute most important reason these models didn’t tell us that Trump would win? The polls were wrong. You’ve heard “Garbage In, Garbage Out”? That’s what happened here. Most of the polls were just plain wrong (and that’s a whole other topic of discussion I won’t get into here). If the numbers you put into a good model are not accurate, the output is going to be inaccurate. Silver accounted for some of the reliability of the polls, but only in relation to each other, and very few appear to have been accurate.

Finally, even the best models will always have a hard time accounting for the complexity of human behavior. I often felt the futility of it all as a researcher; as soon as we’ve figured out something, it changes. We do keep trying and we do make progress, but I think there will always be surprises. Humans will always be at least a little unpredictable.

This election was unprecedented in a myriad of ways. Very little about it was “normal”. It shouldn’t be surprising that the outcome wasn’t within the expected range.

I said it before the election and I will say it again now: I have more faith in Nate Silver’s model than in any other. The approach, the math has proven itself (no pun intended) in other applications in which there is a lot more data (e.g., baseball). But no model can overcome the problem of too little data.

One more thing before I go. You might notice that I have made no statement about the value of these predictions. I’ve heard and read a lot of comments, both before and after the election, suggesting that sites like 538 provide only useless information. On the one hand, I agree. I think that we love these sites because human beings just love information. It makes us feel more in control if we can predict outcomes, even if we are helpless to change them. On the other hand, they seem to be useful in sports and other applications, so I have to believe that they are useful in politics somehow. I don’t know, though; I’m not a politician. And thank goodness for that.

I really, really hate infographics.

Really.

They’re worse than memes. Well, I guess technically they are memes. They are notoriously inaccurate. They are usually agenda-driven and often spin facts to the point of wrongness. But mostly I just see them as click bait.

I have less disdain for listicles, but they sometimes bug me, too.

Yes, babies sleep a lot. Bet you didn’t know that!
/sarcasm

The only source is a link. The link goes to–surprise, surprise–an infographic.

Ugh.

Actually, it goes to an infographic that was re-posted from another site where I assume it originated (embedded below).

The graphic itself cites sources at the bottom, so I dug in. But before I talk about the source material, let’s take a look at the list.

1. We can only dream about faces we have already seen, whether we actively remember them or not.

Um, what? As opposed to faces that we haven’t seen?

No, we can’t “see” the faces of real people we’ve never seen, but we are perfectly capable of making up faces in our dreams. People we make up are certainly people we’ve never seen.

2. When dolphins sleep, only half their brain shuts down. The other half stays awake to help with breathing cycles.

True. Dolphins and whales sleep one hemisphere at a time.

3. Men have dreams about other men 70% of the time. But women dream about women and men equally.

I haven’t seen this in any of my textbooks, nor have I read any studies that confirm this. I’m skeptical, but it’s one of those factoids that, if true, I’d think “so what?”

4. While you are sleeping your body recharges, your cells repair themselves, and your body releases important hormones.

True.

5. Parasomnia is a type of sleep disorder that makes you do unnatural movements despite being asleep. Crimes committed on parasomnia include: sleep driving, writing bad cheques, murder, child molesting and sexual assault.

True (although they’re talking about a specific type of parasomnia) and FASCINATING. When I taught introductory psychology, we spent the most class time on parasomnias because it’s so fascinating. Share your sleepwalking stories in the comments because I never tire of them.

6. 12% of people dream only in black and white. This number used to be higher but since the advent of color television, more people dream in color than before.

Another bit I’ve never heard. I wouldn’t be surprised, but I also don’t know why it matters.

7. Dreaming is normal. People who do not dream generally have personality disorders.

This is bullshit. First, dreaming isn’t just normal, but it’s something that everyone does. People who say that they don’t dream simply don’t remember their dreams.

And no, failing to remember your dreams absolutely does not mean that you have a personality disorder.

No. Nuh uh. Not remotely.

8. Sleep positions may determine your personality.

Um, no. No “may” about it. This is about as true as saying your favorite color determines your personality.

9. 1 in 4 married couples sleep in separate beds.

Again, I don’t recall ever hearing about or reading a statistic on this, but 1 in 4 seems awfully high to me. I wouldn’t be terribly surprised. There are lots of reasons for it. But 1 in 4? I’m skeptical.

10. British soldiers were the first to develop a method in staying up 36 hours without sleep. When fatigued, they put on special visors that emulated the brightness of a sunrise and it woke them up.

I can’t confirm this, but it’s plausible.

11. Longest sleeping mammals are koalas (22 hours) and the shortest sleeping are giraffes (1.9 hours taken in 5-10 minute sessions).

May be true. But seriously, who cares?

12. You need different amounts of sleep depending on your age. Babies need the most (16 hours) and people over 65 need the least (6 hours).

Although it is true that age matters when it comes to sleep requirements, there is a lot of variability among individuals and the recommendations have changed in recent years. In general, the older you are, the less sleep you probably need, but most Americans are at least a little sleep deprived.

13. You’ll die from sleep deprivation before food deprivation. It takes 2 weeks to starve, but 10 days without sleep can kill you.

I have to wonder where this one came from.

First, the term “sleep deprivation” includes too little sleep, not just no sleep.

Sleep deprivation can have very serious health effects. However, one does not usually die from lack of sleep. It’s sort of like dying from holding your breath. You can’t. You’ll eventually pass out and your body will resume breathing without your will to do so.

One can certainly die from the health problems that sleep deprivation contributes to or may even cause, but in 10 days? Not likely. There aren’t a whole lot of documented cases of total deprivation, so I can’t even guess where this number came from.

At most I think that we can say that sleep is necessary for good health. Might even say that you don’t want to go without it, but if you do, you just mind find yourself in a situation in which you have no longer have a choice.

14. Blind people can still see images in dreams. Those born blind experience dreams involving emotion, sound, smell, and touch instead of sight.

Well, of course. Our brain activity when dreaming is very similar to waking states. The biggest difference is that the information isn’t coming in through our senses. It makes sense that the experience would mimic the life our brain has developed to experience.

15. Within 5 minutes of waking up, 50% of your dream is forgotten. Within 10 minutes, 90% is gone.

This is just made up.

16. 1 out of 50 teenagers still wet their beds.

I looked at about a dozen sources and all put the number at 1-2 in 100, so I think 1 in 50 is the highest in the range of estimates. But the statement is a bit misleading in that it suggests that it’s a chronic problem for that many teens. The estimate includes those who experience a bed wetting incident once in their teen years, which is probably the majority of cases.

So that’s it. A few fun facts, some boring and unverified factoids, and some outright bits of bullshit. That’s what happens when you regurgitate something that was regurgitated by someone else using something that was copied from something else that was created by someone who basically made stuff up.

Now let’s take a look at the ‘citations’.

The source list for the infographic is printed on the bottom of the graphic itself, so I dug in. There are 16 citations and 16 list items, but they don’t appear to be related to one another in a one-on-one fashion at least.

The first source is a site called “MEDPAGE TODAY”. It’s not a bad piece, but I can’t find an item on the infographic that corresponds to what’s discussed on the page.

The next three sources resulted in error pages. The second of these defunct links is to–another surprise–a LISTICLE. Someone was kind enough to post this one in a forum, so we can see the original content. Unfortunately, no citations were included.

The next item, sleepapnea.org, documented only the main page of the website. I cannot tell which “facts” were gleaned from this source.

Item number 6 is a Slate piece about sleeping with your dog. There is nothing in the infographic or the listicle about sleeping with your dog.

The next three are news articles about parasomnia. Two are dead-ends. So three links to support one item on the list and two are outdated (giving the author the benefit of the doubt).

The next link is incomplete, so there’s no way to access it, and the one that follows that one is the same as link number 2.

The next link might be the jackpot. It’s a site cataloging “interesting facts about dreams”. It lists 99 items from seven sources. One of those sources is specific and the rest are for-the-masses books like “The Big Book of Dreams”.

Then there are a few more dead ends and a final page that doesn’t appear to correspond to anything on the list.

Okay, so now we have some idea of where this stuff came from (don’t say it), but not much.

From Visually.

A skeptical friend sent me an interesting link this week. The headline makes the bold claim that a study suggests that people who like black coffee are more likely to be psychopaths. I’m sure you can guess my initial reaction: skepticism.

So let’s look at the research.

First, the choice of singling out black coffee is journalistic bullshit. The research is not so specific. Instead, the researchers categorized foods by taste and included coffee as a member of the “bitter” group.

Now, I think that’s a questionable categorization. Some coffee, especially strong coffee, can be bitter. However, when I hear or read the word “coffee”, I don’t think “bitter”. And that is directly related to the biggest problem with the study itself: the method was survey, not taste test. In other words, we don’t know if people truly preferred the tastes themselves. Self-reports of such things are problematic, especially when the food categorizations themselves are arguable.

What the researchers did: an online survey asked participants to rate their preferences for a list of food items, then to complete a number of scales such as personality inventories and measures of malevolent traits. What they found: a strong correlation of preference for sweet foods with agreeableness. Slightly weaker, but robust correlations of preference for bitter foods with psychopathy and “everyday sadism”.

So, in general, what they found is that agreeable people tend to prefer sweet foods more and bitter foods less than disagreeable people. And those who score higher on measures of psychopathy and sadism tend to prefer bitter foods more than those who score low on those measures.

The study itself isn’t bad and the findings are interesting, but it’s very, very limited due to the method.

There are a number of possible explanations for these findings. Taste preferences come from both genetics (our taste buds vary) and habits (what we eat shapes what we like to eat). Likewise, personality traits are like most human traits in that they are partly determined by genetics. It is possible that the small correlation seen here is at least partly a clustering of genetic traits.

It’s also possible that years of munching on radishes makes people cranky and disagreeable.

It’s also possible that rating foods primed people to associate those foods with personality traits (e.g., the authors note the stereotype of bitter foods being associated with bitter people), thereby affecting the outcomes of some personality measures–something the study authors fully acknowledge.

The authors also admit to another limitation of the self-report measure:

…in preferring bitter tasting foods more than less sadistic people, everyday sadists may perceive them as positive due to their potential to cause distaste [as opposed to their own preference], that is, to cause a negative experience in other people.

However, the findings are consistent with other research. For example, one study linked PROP sensitivity (Supertasters) with unpleasant emotional reactions to film clips depicting aggression. PROP Supertasters have a genetic predisposition to experience phenylthiocarbamide (PTC) as bitter, whereas most people find it tasteless. It is a bit of a stretch to conclude from this finding that aversion to bitterness is associated with stronger empathy, but it’s an interesting finding nonetheless.

Still, I have to wonder–and pardon my language here, but–who the fuck cares?

It’s not as if this research suggests that you should avoid sharing a table with someone at Starbucks simply because they order the Sumatra blend instead of a pumpkin spice latte or a mocha cappuccino. It just doesn’t.

Sagioglou C, & Greitemeyer T (2015). Individual differences in bitter taste preferences are associated with antisocial personality traits. Appetite, 96, 299-308 PMID: 26431683

Note: This post also appears on Insight, the official blog of the Skeptic Society.

A few months ago I wrote about the psychology of vaccine denial. In the post I discussed two publications, one of which (Nyhan, et al.) found:

Corrective information reduced misperceptions about the vaccine/autism link but nonetheless decreased intent to vaccinate among parents who had the least favorable attitudes toward vaccines. Moreover, images of children who have MMR and a narrative about a child who had measles actually increased beliefs in serious vaccine side effects.

None of the interventions increased parents’ intent to vaccinate.

Then, a couple of weeks ago, a friend sent me a link to this piece describing research which seems to contradict that finding. The authors (Horne, et al.) concluded that

…highlighting factual information about the dangers of communicable diseases can positively impact people’s attitudes to vaccination.

These two conclusions seem to contract each other. Which should we believe?

Many times this question comes down to the quality of the research. In this case, I believe these are both fairly well-designed studies. One, however, is more precise than the other in several ways. I believe that precision highlights the complexity of the issue as well as giving us a better idea of the direction that vaccine promotion should take.

Let’s look at the differences in sampling and method between the two studies.

The Horne study sampled 315 men and women. In the Nyhan study, the final sample was 1759 parents with children under the age of 18. In most research, 315 subjects is more than sufficient and more is not always better. The danger in larger samples is to find effects that are statistically significant, but not practically significant. However, when comparing conflicting findings, it is best to bet on the side of the larger sample.

Then there’s the question of limiting the study to parents. Although Horne compared parents to non-parents and found no significant differences in attitudes or effects, noise is noise. These two groups of people vary, and the attitudes of non-parents are not particularly relevant. Limiting the study to parents would give me more confidence in the robustness of the findings and their application in real-world activism.

Still, if both are reasonably well-designed studies by competent researchers, the end results should not contradict each other. So there must be more going on. And there is.

For one thing, this is a great example of how complex social sciences are. We should never make policy decisions based on a single study and this demonstrates why. Replication, especially with variants of measures and materials, is essential to learning the best methods of persuasion.

For another, these studies differ in more than just sampling techniques. The Horne study is much simpler and, in fact, oversimplifies. Nyhan, et al. included three outcome measures, each addressing a specific attitude:

1. The belief that vaccines cause autism.
2. Perceived risk of side effects from vaccines.
3. Intent to vaccinate one’s child/children.

By contrast, the Horne study involved a single measure which combined answers to five specific questions (such as “I intend to vaccinate my child.” and “Doctors would not recommend vaccines if they were unsafe.” to come up with a more vague “vaccine attitudes” scale. Even if the answers to these questions are highly correlated, how interventions affect those answers may be very different. They certainly were in the Nyhan study. And if “effective” is defined as increasing intent to vaccinate, then the Horne study does not answer the question it purports to answer. Personally, I am more interested in intent to vaccinate than I am in any other aspect of “vaccine attitudes”, so the Nyhan study’s findings are much more meaningful to me.

In general, it is best to measure outcomes of interest as specifically as possible, but of course the more outcomes a researcher studies, the larger the sample must be.

Finally, and perhaps the most important difference between these two studies, is the timing of the experimental portion. When measuring the effect of treatments or interventions on attitudes, an experiment should be spaced over time. A researcher will measure the attitude, then wait before applying a treatment and measuring the attitude again. When polled about attitudes, those attitudes are brought to mind. This affects our receptiveness to relevant information in complex ways, ways that vary based on a number of other factors such as the strengths of our attitudes and the way the questions are worded. However, allowing subjects to forget about the initial survey provides a more accurate picture of how people confronted with information in the real world may respond to it.

The Horne experiment was conducted a day after the initial screening while the Nyhan experiment occurred about two weeks after initial screening.

My conclusion? I think the issue is complex, but while Horne’s findings appear easier to understand, Nyhan’s findings are more specific, answer more interesting questions, and can be more easily viewed within the framework of well-established knowledge about human decision-making (e.g., cognitive dissonance).

That, and we need more research if we are to develop effective ways of increasing vaccination rates.

Horne Z, Powell D, Hummel JE, & Holyoak KJ (2015). Countering antivaccination attitudes. Proceedings of the National Academy of Sciences of the United States of America, 112 (33), 10321-4 PMID: 26240325

Nyhan B, Reifler J, Richey S, & Freed GL (2014). Effective messages in vaccine promotion: a randomized trial. Pediatrics, 133 (4) PMID: 24590751

Sometimes I wish that I didn’t have a nagging conscience so that I could make millions selling crap at a 6000% profit just by claiming that it does something it doesn’t. Unfortunately, I’d be embarrassed by the first skeptic to challenge me. Oh, and then there’s that pesky fact that I’d be scamming people and promoting pseudoscience.

Anyway, there seems to be no end to the things you can convince people that some token item embedded in $.08 worth of rubber can do.

Last week I stumbled over Sharkbanz.

Screenshot of the Sharkbanz website. The band can be worn on the wrist or ankle.

Sharkbanz are rubber jewelry similar to a Fitbit Flex. Instead of a motion sensor, however, Sharkbanz contain magnets. The company’s founders claim that these magnets repel sharks.

Yes, you read that right. Magnets in a rubber bracelet repel sharks.

How does it work? Well, let’s look at what they claim.

Screenshot of the Sharkbanz website, explaining the technology.

Summary:

• Uses magnets
• No batteries, no electricity, no moving parts
• Rated for 200 meters. What happens when you dive below that? Does the magnet implode or the rubber erode?
• For ages 5 and up. So if you’re 4, you’re some Shark’s dinner I guess, because for some reason it is just not safe for those under 5. By the way, choking hazard warnings are usually for children under 3, so this arbitrary value makes little sense.

The idea is that sharks are annoyed or frightened by the electromagnetic field emitted by the magnet because of their keen electroreception capabilities.

Okay, so I’m not a shark expert, nor am I a physicist or an engineer. For this reason, I’m going to tread a little lightly on the examination of whether the idea is plausible and focus on whether they have demonstrated that the things work. But I will admit to a bit of skepticism given the knowledge I do have. Some of my thoughts:

• I don’t know that the differences between magnetic fields, electrical impulses, and electromagnetic fields matter, but the owners of Sharkbanz imply that any electromagnetic energy is disturbing to sharks. If that were true, then light would bother them. Clearly, just as the human eye only senses electromagnetic energy within a range of wavelengths, sharks’ electroreceptors are limited in range. Yet the website says very little about what research and testing they have done to determine whether sharks even sense their magnets. The most that I can find is a link to a list of tested species on the website of a group called SharkDefense. There is zero detail provided in this list and no citations to peer-reviewed literature about these tests. The site does cite some literature in a section which explains the theory (which, again, I am unqualified to adequately challenge, but it looks mostly correct), but provides no reason to believe that the idea because sharks have electroreception, they will be repelled by this specific small magnet wrapped in a rubber bracelet. In fact, take a look at the size of the magnet the claim to have tested (again, no citation for this): Click here
• Sharkbanz presents no reason that a man-eating shark would have for avoiding the electromagnetic field emitted by their magnets. Just because sharks have electroreceptors doesn’t mean that some random magnet is going to affect their behavior. Perhaps if they made an effort to produce some magnet that emitted a field that mimicked the energy put out by a predator it might make sense, but do bull sharks even have predators? I don’t know, but I’m skeptical.
• Then there’s the fact that the magnet is encased in rubber.

So if you’re an expert on sharks or the physics that apply here, please feel free to comment with your 2 cents, but I’m going to focus on their “scientific testing”.

Sharkbanz are a fun, easy way to add peace of mind to the everyday ocean swim, surf, or snorkel, but make no mistake: the science is real.

Bull*cough*.

This technology has been tested extensively on over ten of the most common predatory shark species…

On the website, there are several references to videos that supposedly show this “extensive testing”. However, most of the videos I’ve found when I click those links are media interviews in which the owners say very little. There are certainly no published articles from peer-reviewed journals, not even some about electroreceptors in sharks that don’t really support their claims. They don’t even try to fake it. What I did find are a few unconvincing videos which show a couple of guys dropping a buoy into the water and watching a fish or two swim away from it.

In this video, the only one that claims to show testing, the voiceover claims that they tied a bracelet to a buoy “in order to be scientific” as well as safe before throwing it into the water with reef sharks. Then, he put two shark bands on his ankles and got into the water which, he claims, induced “obvious repellent reactions when the sharks approached the bands on my feet”. Finally, they “decided to get more of the controlled type of testing” by tying the bracelets around lead weights, which he claims produced “really astonishing repellent behavior”.

Well, I’m sorry guys, but scientists everywhere are laughing at you. They are giving you the virtual equivalent of “there, there” pats on the head and “aren’t you cute? Do you want to be a scientist when you grow up?” Except that given the voice on this video belongs to Dr. Patrick Rice, Senior Marine Biologist at SharkDefense, you’ll have a hard time convincing me that you don’t know quite well that you’re not scientifically testing anything. This is a marketing video and nothing more.

A very short, far-from-exhaustive list of plausible alternative explanations for what the video shows:

• fish in the shots are not darting away from the bracelet, but rather toward food that was just dropped in the water
• cherry-picking shots in which fish are swimming away
• fish examined the item (or the person) and immediately recognized that it wasn’t food (or preferred food)
• fish avoid things hanging from buoys or anything that looks like the item (rather than being repelled by the magnet)

Can you guess what a good, scientific test of this product might entail? A double-blind, placebo controlled study. In other words, you’d want to simulate the movement of a human being as much as possible. You’d want to stop chumming the water after you’ve introduced the treatment (the bracelet). Most of all, you would need to compare the fishes’ behavior toward a Sharkbanz bracelet to their behavior toward a placebo–a bracelet without the magnet. Also, you would need the people recording and judging the behavior to be blind to the condition (they cannot know whether the fish are reacting to an “active” bracelet or a placebo). Without that comparison all of these tests are worthless.

In sum, these guys are charging $60 for heavy rubber bracelet that people will buy because it claims to protect from shark attacks–an extremely rare event with a high fear factor.

In the U.S., it takes a lot of time and money to get a group like this to stop making ridiculous claims, but I have to wonder how this company is allowed to market and sell in Australia, given what happened with Power Balance.

By now, you’ve either wondered just what color the dress is or you’re living under a rock in the middle of a deserted island with no internet and the first thing you chose to read when you were back online is my blog. Weird, but awesome.

The last few days have seen a flurry of education about our visual system and I won’t duplicate too many efforts here. But I would like to talk a bit about the illusion and the way the internet exploded with pseudoexplanations and other BS as a result. I’m going to spend a little more time on what this illusion does NOT mean.

First, the dress…

http://swiked.tumblr.com/post/ 112073818575/guys-please-help-me-is-this-dress-white-and

When the ugly white and gold dress first appeared in my feed last Thursday, I had the same reaction that I’m sure many of you did: this is a hoax.

I quickly determined that it was indeed real, immediately saw additional pictures of the blue and black dress, both in an online store and on the woman who wore it (BTW, as ugly as that dress is, she manages to pull it off), and started to think about what was happening. I fully accepted that the dress was actually blue and black, but I couldn’t perceive it that way in that photograph to save my life. It was fascinating!

At this point I should note that I have studied color vision and color perception since I was an undergrad. My first real experiment involved asking people to rate the facial expressions of expressionless faces drawn on backgrounds of different colors. The purpose of the study was to test the claims made… well, everywhere… that colors affect people’s emotions. I continued that line of research for more than 10 years and won several awards for it. My master’s thesis was titled “The Physiological Effects of Color on Human Emotion”. A summary of many years’ worth of findings is available here.

I also taught cognitive psychology (a portion of which involves sensation and perception) for several years and supervised a number of student experiments about color, some with very interesting results. What I am trying to say is that I have a good handle on what is causing the illusion. I was still mesmerized.

The illusion itself is not what I find so fascinating. What’s fascinating, and what caused it to blow up the internet, are the individual differences. Most polls only offered two possibilities, with about 70% claiming to see it as white and gold, 30% as blue and black, but I counted about 1 in 5 in my Facebook feed who said that it was sometimes white/gold and sometimes blue/black. I can recreate very similar illusions quickly using a basic drawing program, but illusions tend to be fairly universal. While we see some differences across cultures, mostly due to differing environments, I don’t think that I have ever seen people in the same household, looking at the same picture, perceive an image so differently and in a stable manner. Yet my youngest son saw only blue and black while the rest of the house saw white and gold. When it comes to ambiguous figures, such as the famous figure/ground image below, most people can perceive the image either way and reverse it at will. Of those who said that they sometimes saw the dress as white/gold and sometimes as blue/black, nobody I talked to said that they could switch the perception at will.

Do you see faces or a vase?

On Friday, post after post tumbled through my Facebook feed. The first posts I saw picked apart the image, extracting information about the color of the pixels. Well, that stuff is mostly irrelevant. Perception is much, much more than the simple sensation of the color of light hitting our eyes. The color of the dress in the photograph does not tell us the color of the dress (which we know from other sources) or why some people perceive the dress’s color as it is (blue/black) and some so differently (white/gold). Both, btw, are considered illusions since neither matches the pixels in the image, but nobody should perceive the dress as the pixel colors because they wouldn’t perceive it that way in person. Confused yet?

Next came all of the explanations. Some claimed to show us how we could perceive the dress differently, replete with not-quite-right explanations for why it’s happening. Yet I have met nobody who could force a different perception, at least not for more than a fraction of a second, much less using the methods suggested. Those doctored images just looked like doctored images to me. They did not change my perception that the dress was white with gold lace. Many gave some pretty bad explanations (those who saw blue/black do not have better color constancy). Some suggested that one’s sleep cycle could explain individual differences (it can’t). This guy says:

Your eyes have retinas, the things that let you interpret color. There’s rods, round things, and cones that stick out, which is what gives your eye a textured appearance in the colored part.

And that, my friends, is how a little bit of education can lead to dangerous things like deciding not to vaccinate your children. He has either skimmed Wikipedia’s entry on color theory or was half asleep through a half hour lecture in an intro psych class and now thinks he understands how eyes work.

And as of this writing that bit of BS has been RTed over eight thousand times.

My favorite bogus explanation: Mood. The color you see depends on your mood. That’s the one my kids heard the most during school that day.

And the jokes. Oh, the jokes. Some, like XKCD, were 10 kinds of awesome (make sure you read the alt text, and if you don’t get it, read this post). Others were not so funny.

Then, late Friday evening, after I’d taken a bit of a break from “the dress”, someone sent me this video and all hell broke loose. The video content isn’t relevant; it’s just a fun look at how to correct the photo. What is relevant is that suddenly the dress was BLUE AND BLACK. Not brown, not like an altered photo of a white and gold dress, but blue and black–the same royal blue that is in the other photos. I thought maybe the image in the video had been altered, so I pulled up a blog post with the original picture and it looked the same. I Googled “what color is this dress” and looked at the images. I saw a sea of blue/black dresses with one white/gold one in the corner. It looked to me like someone had replaced most of the pictures on the internet!

Since then the dress color switches for me. I usually see it as white with gold lace, but now and again it’s deep blue with black lace.

And I know why, yet I am helpless to control it.

And over the weekend, I started to see about-the-dress-here’s-some-more-BS-about-color-vision posts. Business Insider, which is becoming more and more like Buzzfeed every day, posted a number of pieces of varying quality. One of them had the unfortunate title, “No one could see the color blue until modern times”. The piece itself isn’t bad. It speculates about the effect that naming colors has on perceiving colors. There is some evidence that cultures which fail to distinguish between blue and green perceive fewer distinct hues in that area of the spectrum, but to extract from that the idea that human beings didn’t “see” blue until they had a name for it? Well, that’s a preposterous leap that’s actually a bit silly. Why would they name something that didn’t exist?

Another which has been picking up steam in the last couple of days is this incredibly bad piece on linkedin which makes the bold claim that 25% of people have a 4th cone that allows them to “see colors as they are”. It’s a very strange thing to claim–to “see colors as they are”, since colors are simply the brain’s interpretation of wavelengths of light. In that sense, colors aren’t. They kind of don’t exist. The article talks about tetrachromacy, which is a real phenomenon, but the author basically made up the rest. I cannot fully explain color vision (or vision in general) in a blog post, but I can tell you this is wrong. Also, tetrachromacy is extremely rare, at least in functional form, and her “test” is totally bogus. The author calls herself “Prof. Diana Derval, expert in neuromarketing”. I know that it’s trendy to slap “neuro” onto everything, but really? Neuromarketing? Ugh. I first saw and responded to the piece on Facebook Sunday night and by Monday afternoon Snopes had covered it (they did a nice job, too, but they usually do), but that didn’t keep it from going viral.

So what is going on with the dress?

Well, several pieces have explained it with relative accuracy. I said I wouldn’t duplicate those efforts and I won’t. I will instead recommend that you read/watch more than one of those links, take a look at the additional illusions that I created below, and internalize this perfect summation:

“It’s an illusion,” said [David] Whitney. “But everything is an illusion.”

And that’s the long and short of it. What we “see” is never, ever exactly what’s out there in the world. It’s our brain’s best interpretation of what’s out there. We are amazingly good at it, but it’s not perfect and perfection is not possible.

The grey bar in the middle is a solid color. We only perceive it as darker on one side than the other because of contrast with the surrounding color.

Although one bird appears to be more orange than the other, they are identical.

In 1987 I worked as a receptionist in the real estate lending office of a savings and loan. A savings and loan, for those too young to remember, is a bit like a bank which invests most of it’s money in mortgages (think Bailey Building and Loan). Shortly after I was hired the whole savings and loan industry collapsed, mostly due to questionable commercial lending practices and other bad investments. My office was one of the last to close, so for months we had plenty of time for office chatter.

In our office of about 20 people, and me, about half visited a psychic on a regular basis. They saw the same woman. I often heard my co-workers going on and on about what Sally (I don’t remember her name) told them and when they would go to see her next. I tried a few times to talk to them about psychics. I told them about all of my failed experiments with Zenar cards as a kid. I told them about my experience in high school, when a man came to my psychology class, did a few tricks, then told us he wasn’t psychic, but a magician. I told them about the challenge the Bay Area Skeptics and James Randi (and probably others) offered to anyone who could demonstrate psychic ability.

The looks I got gave me chills. These people were not simply unconvinced. They hated me for what I was saying.

And what they said in response was hurtful. I was just a kid (I was almost, but not quite, 21) with no experience in the world. What did I know? I’d learn.

Fast forward a few years to around 1992. At that time I was working at a software company and was fairly well-respected in my position as an administrator. I called most of my coworkers friends and the company had low turnover, so we had known each other for some time. But I had no idea I was among so many believers.

A picture of me (blown out by the flash), at the Monte Carlo party, before I saw the psychic.

The company threw a daytime party that year in an empty space next door. The theme was “Monte Carlo” and we all got dressed up, imbibed, ate, and played blackjack for a few hours. And visited the psychic.

I never understood why the person who planned the party included a psychic. I didn’t realize that psychics were part of the Monte Carlo culture. But there she was. A psychic. At our Monte Carlo office party. And several friends could not stop talking about it.

“Don’t you want to hear what she has to say?” several people asked.

I said “no” every time. I told them about all of my failed experiments with Zenar cards as a kid. I talked about my experience in high school. I told them about the challenge. I talked about the lack of evidence for psychic abilities.

Nobody got angry or hurtful this time. Instead, they wanted to prove to me that it was real. They begged me to take a turn. So I did.

I sat down at her table and vowed to myself that I would not scoff or laugh. I would just answer her questions truthfully and try not to give her any clues. I have a pretty good poker face.

Sylvia (I don’t remember her name, either) really only asked me one question of interest and it was her downfall.

She asked, “Is your mother more like Betty Crocker or Susan B. Anthony?”

That was easy. “Betty Crocker,” I said.

Sylvia then went on for about 20 minutes while I sat, amused and expressionless, listening. My friends/coworkers listened, too. Sylvia lectured me about my inability to break out of the character that my mother had modeled for me–the meek and timid woman who never felt strong enough to stand up for herself or speak up for others. She told me to find my own voice, that I needed to be more assertive. And so on…

I thanked her, then got up and headed back to the office to get some work done.

I thought that this reading would convince my friends that psychics were frauds. You see, I had built a reputation at that company. I was certainly not timid. I was not quiet. I was not meek. I was in fact… kind of a bitch. But I got the job done precisely because I was assertive. My personality has not changed much.

And my mother? Well, the mistake Sylvia made was asking such a simplistic question. I answered “Betty Crocker” because my mother took cake decorating classes (and made the most amazing doll cakes for me every year; she made my wedding cake, too) and is a very talented seamstress. Yes, she cooked and baked and sewed and got involved with the Navy Wives Club and volunteered as a nurse at my elementary school. She was also nothing like Susan B. Anthony. She didn’t burn her bra or march for women’s liberation. BUT, she was tough. Still is. She took cake decorating, but she also took auto repair classes and she taught me how to install car radios. My mother is not meek, not timid, and certainly has no trouble asserting herself. Nor do I.

And I thought it was blatantly obvious that the psychic had totally blown it.

But my friends still gathered around me, giggling excitedly about how great Sylvia was. When I said, “But she got everything wrong,” they dismissed it. They said, “She’s just telling you that you’re on the right track!”

When I objected with, “But my mother is nothing like that,” they said, “Well nobody gets everything 100% perfect.”

They talked about all of the vague things that she’d said in their readings and what they thought all of those things meant. They were drenched in the excitement of belief.

I shook my head and trudged back to my office alone. If that demonstration didn’t convince them, I didn’t know what would. I was sad about that for a long time.

It would be years before I went back to school to study psychology, teach, and look for better ways to promote skepticism.

People who believe in psychics don’t care whether they saw the psychic at their place of business, at a party, or at a county fair. They don’t think that matters because a psychic is a psychic. They don’t think that “party psychics” are just there to give bogus readings for fun. What fun would that be, anyway, if you don’t believe they are real?

“Party psychics” may not bilk a mark out of thousands of dollars, but they take money in exchange for pretending to know things they cannot know. They are no less fraudulent in their actions and just as guilty of promoting harmful superstition. This is not harmless.

Let me cut to the chase: No.

Full disclosure: I own a Fitbit Flex™. I haven’t worn it in months, but I do like the thing. It’s a long, boring, irrelevant story why I’m not wearing it, so I won’t get into that. Just know that I am sincere when I say that I like my Fitbit.

I don’t think my Fitbit is telling me exactly how far I’ve walked or how many calories I’ve burned. I do think that it gives me a ballpark figure that can be used to compare one day to other days. I don’t think that it’s telling me exactly when I am sleeping, either, but the information about how restless I am some nights compared to others is interesting and even sometimes useful.

I also like that it buzzes on my wrist to wake me up in the morning.

Recording some basic information about my daily activity, giving me a reason to record what I eat, waking me up in the morning, and reminding me to get off my ass. These are not big goals and perhaps I don’t need a $100 gadget to accomplish them, but is the device bullshit?

Mother Jones sure seems to think so. Last week the site posted a piece titled “Science Says Your Fitbit is a Joke“. When I searched for more reports on the topic I found a Jezebel piece titled “Your Fitbit is Bullshit, Says Science“, and one on RYOT titled “Science Says Your Fit Bit’s Full of It“.

Since I don’t particularly trust any of these sites as a source of accurately-interpreted science news, especially those which appear to be trying a little too hard to find clever, edgy headlines, I was skeptical.

The Jezebel piece is easily dismissed as simply lazy blogging. The author links to Mother Jones, paraphrasing one of the main points of the piece (bold mine):

Mother Jones points to a new study showing that your iPhone or device of choice does just a good a job[sic], if not a better one, at doing things like tracking calories and measuring activity.

Except that’s not what the Mother Jones piece said. It reads:

But, according to a new study published Tuesday in the Journal of the American Medical Association (JAMA), the apps on your smartphone do the job just as well, or even better—at least in terms of measuring your steps and your calories.

So Jezebel incorrectly paraphrased Mother Jones. And they aren’t the only ones. RYOT wrote this:

A new study came out showing that iPhone apps and other (much cheaper) devices do a better job than the bands at tracking your activity, steps, and even calories.

Even assuming Mother Jones is correct, these paraphrases are not. Walking is, of course, an activity, but it’s only one type of activity. Mother Jones was correct in noting that smartphones can do the work of pedometers, but Jezebel and RYOT are overstepping (pun intended). Wearables such as the Fitbit Flex™ and Jawbone Up™ can track your activity while you are swimming, bowling, or doing jumping jacks. Can your smart phone do that? Well, I suppose if you’re holding it in your hand, it can, but who does that? Also, if you don’t sleep with your smart phone strapped to your body, it cannot track your activity during sleep.

But is Mother Jones right? Might smartphones do a better job? Well, let’s find out.

Although Jezebel linked to the original source, a press release by University of Pennsylvania School of Medicine, it does not appear that the author read the press release, nor did the author of the RYOT piece, who linked only to the press release (failing to even give Mother Jones credit for the thoughts expressed). There is no reference to smart phones doing a better job at anything, much less a list of activities. The only study which is discussed is the one described in the Mother Jones piece, which examined the accuracy of several devices at counting steps and found all to be similar.

But let’s get to the bottom line. Is it a fair assessment that science says these devices are bullshit? A Joke? Full of it?

First, the study discussed involved counting steps. That’s it. There is absolutely no comparison of different devices in regard to tracking calories and no examination of other activity or reports such as distance walked. The study was fairly well-designed–something that does not come across in the reporting. Although there were only 14 participants, each wore every device at the same time and each walked both 500- and 1500-step trials twice.

Here are the results from the 500-step trials:

The results of the 1500-step trials are nearly identical. Now, it appears that the Fitbit One™ and the Fitbit Zip™ are spot-on, and the fact that the error bars are practically non-existent is pretty impressive. Unfortunately, the authors failed to do any sort of statistical analysis at all. Instead, they stated these results:

We found that many smartphone applications and wearable devices were accurate for tracking step counts. Data from smartphones were only slightly different than observed step counts, but could be higher or lower.Wearable devices differed more and 1 device reported step counts more than 20% lower than observed.

Given the error bars for the rest of the devices, including the Fitbit Flex™, I’d believe that most did not differ from one another significantly, with the possible exception of the Nike Fuelband™, which apparently sucks. However, there are two very clear winners, with the Digi-Walker™ close behind. A simple statistical analysis would have confirmed this.

Regardless, it is very clear that smartphones do not outperform most of the wearables. What is really in question is whether people use the devices and use them to their advantage. This may be questionable, but it has hardly been decided by science and it has certainly not been decided in favor of tossing one’s Fitbit™. The JAMA article concludes:

Increased physical activity facilitated by these devices could lead to clinical benefits not realized by low adoption of pedometers. Our findings may help reinforce individuals’ trust in using smartphone applications and wearable devices to track health behaviors, which could have important implications for strategies to improve population health.

So there’s hope that devices, whether they are wearables or just smart phones, will translate to improved health for at least some users.

To be fair to Mother Jones, their piece is much more fleshed out than the other two, covering more than this one study. In my opinion, the Jezebel and RYOT pieces simply plagiarized Mother Jones. However, I saw nothing in the rest of the piece to warrant such harsh criticism of wearables.

Now after writing most of this I came across this wonderful piece on Mashable that was posted yesterday. I could have simply linked to it, saying “read this” because it’s pretty much what I have said, right down to a comparison to the game of “telephone” that I just edited out of this post.

In the end, I have to agree with its author, Chris Taylor, who opens with:

There are poorly designed scientific studies, and then there’s poor reporting on scientific studies by journalists who should know better.

So this Good Morning America piece showed up in my Facebook feed the other day touting the sensational headline “Odd-Defying Babies Born 10:11 12/13/14″.

Now, I think it would be adorable to have a baby born on 10:11 12/13/14 (in America, of course. In Europe, that would be 10:11 13/12/14, which just doesn’t hold the same cuteness). Human beings love the symbolism that comes from conventions such as labeling and ordering. My good friend and fellow Skeptic, Ani Aharonian (Insight blogger and guest blogger here) was married on 5/8/13 for a reason (can you guess?). But “odds-defying”? No. These babies defied no odds.

Odds are a property of something that is yet to be. They are really only valuable as a means of predicting something and have no value after the event has occurred. This is a bit like post-hoc (after-the-fact) thinking about lottery outcomes. Did your next-door neighbor defy the odds when they won the lottery? Well, it depends on your perspective, especially in time.

A given individual has an extremely slim chance of winning the jackpot of a lottery. For a Powerball lottery, a single ticket has a one in 175 million chance of winning. However, if 525 million tickets are sold, there’s a pretty good chance that at three, much less one, will be winners. Is it “odds-defying” that your next-door neighbor won? Well, perhaps from your perspective, but not to a total stranger in another state.

Similarly, the question of predicting the odds that any given child is born at that time is quite different from the post-hoc consideration of the odds. The prediction actually changes whether you are predicting the birth from before or after conception. Clearly, if you are carrying a child whose due date is around 12/13/14, the chance are much, much higher than if you have yet to conceive (especially if it’s already April 2013). But nobody even thinks much about these things until after the fact, probably because they are more concern about having a healthy child and mother at the end of it all. Post-hoc, we are really not talking about odds anymore, so unless someone predicted a very different outcome for one of these births, there was no defying of odds.

Well, except this:

Approximately, on average, eight babies are born in the U.S. every minute, so there should be around eight babies in the U.S. right now with the “lucky” birth time of 10:11 12/13/14. There are, according to the article, only two. It’s entirely possible that the author missed some, but let’s say she didn’t. That’s an unusual event. Probably not *very* unusual, as I’m sure the number of children born each minute varies a great deal, but still quite different from what we would predict given what had happened in the past. It’s also a bit interesting that both were born in the mid-west–one in the city of Cleveland, Ohio and the other in the relatively small city of Billings, Montana. Cleveland is not a small town, but why are the biggest metropolitan areas such as Los Angeles and New York not represented? For that matter, they are both girls, yet averages tell us that one should be a boy. Probably because the variability in these events is very, very high and while averages are the best predictors we have, “best” isn’t always very good.

But the reason I wanted to talk about the piece wasn’t just the sensational headline. The contents are pretty eye-rolling, too.

“We knew she was going to be born today [Saturday], we just didn’t know it would be at 10:11 a.m.,” Campbell said in a statement. “Everyone is telling us we should play the lottery. We feel this is a lucky day and are excited to get family photos with Santa.”

It’s a number. It’s cute and all, but really, it’s just a number. It won’t help you win the lottery. It won’t help her succeed in life.

These conventions don’t care who you are. They don’t care where you live or what you ate for breakfast. So don’t complain about the lack of diversity in this sample, either. The universe is colorblind. Sort of.

Those who read this blog in its early days know of my love for all things pareidolia. If you don’t know what pareidolia is, that last link (this one) covers the definition. I have also written an classroom ‘module’ for the JREF on the topic. It can be downloaded for free here (the pdf is quite large; the site is here: http://archive.randi.org/site/index.php/jref-news/2208-new-jref-in-the-classroom-lessons.html).

I have a fairly extensive collection of images (under “Fun For Everyone” on the right), but I have not updated it in a while, so I thought it would be fun to see what people have been seeing. I was not disappointed.

Yesterday, reports of a man seeing Jesus in his chicken made the rounds. According to WNEP, Ernesto Hernandez was sitting down to dinner with his wife at their home in the Poconos (that’s in Pennsylvania) when he saw this:

Screenshot of WNEP broadcast regarding Ernesto Hernandez’s chicken

But that’s not the only recent sighting of Jesus. A few days ago there was a sighting in his mother’s arms in a tree trunk in Iowa:

Screenshot of broadcast by KCCI

Then there’s last month’s appearance in the smoke of a house fire. Well, it’s in a video capture of the smoke and the face appears just as someone in the video says “There’s someone still in the building!” while a guy carries a man out. A neighbor in an NBC News video says, “You see Jesus in the smoke and the person gets saved? That right there is not a coincidence.” Well, yeah, it is, especially when both the “see Jesus in the smoke” part and the “person gets saved” part are questionable. I actually liked what a priest in the video had to say.

But for me, the true face of God, the true image of God, was the man carrying his neighbor out on his shoulders.

For me, that’s simply a demonstration of what humans are capable of. It’s beautiful, but I don’t need God to explain it and I don’t understand why anyone would want to give God that glory when a human being deserves the praise.

And back in August, Jesus towered over HuffPo blogger Fiona Finn (btw, I have to say that I have a seriously difficult time taking seriously someone who describes herself in a byline as “keynote speaker”) in Cape Coral, Florida in cloud form:

via link to Huffington Post