All too often the popular press will jump on a scientific result and shout its praises. On the surface, this is great. The scientists get exposure. The people get knowledge. What could be wrong with that?

Well, it turns out that 99% of the time journalists seem to avoid the dirty work of reading the paper that describes the research in question and simply go for the abstract. Then, they often take one choice sentence from the abstract and extrapolate from there. This, of course, leads to conclusions which are not supported by the data.

Although the most recent example I came across isn’t necessarily an example of this type of journalism, it has the same flavor. This is an interview in Time magazine of Daniel Kripke, co-director of research at the Scripps Clinic Sleep Center in La Jolla, Calif.

The article suggests that those people who sleep between 6.5 and 7.5 hours live the longest and those who sleep less than 6.5 or more than 7.5 have a higher likelihood of dieing early. On the surface, this is terrible for me. I routinely sleep 8 - 8.5 hours per night. I guess I’m already a corpse? Of course, those of you that read the entire article will notice that Dr. Kripke acknoledges that this relationship is NOT CAUSAL. Did you get that? It’s not that sleeping more leads to early death. It’s that, for whatever reason, people who sleep longer also die younger. It does not mean that if I sleep 1 fewer hour I will live longer!

Now, the good news is that this was at least mentioned in the article…but not till pretty far into it. Most people just read the headline and the first paragraph, a dangerous practice. If I took this seriously (as I’m sure many people will), I might uproot my life based on the belief that I’m sleeping too much.

Journalists needs to get some stronger ethical standards when it comes to presenting scientific research and acknowledge things like correlation <> causation early on. Defusing these types of misconceptions will surely lead to a more informed citizenry.

(adapted from Top 10 Reasons to Date an Entrepreneur)

1. Flexible Schedule: Aside from classes, anything goes. Lunch on Tuesday at 2pm? Sure. Dinner on Wednesday at 10pm? Why not! 3 week vacation to Bali? Okay, that’s trickier, but still doable!
2. Good Communication Skills: Classes don’t teach themselves
3. You can tell your friends you’re dating a doctor: Okay, so it’s not doctor in the MD sense, but it still counts!
4. You can go out with your friends and not worry about us: Hours of solitude as grad students means we know how to keep ourselves entertained
5. You can take us out to parties with you: Realize that academic conferences are 15% intellectual and 85% party. We’ve got PLENTY of practice with meeting new folks.
6. You’re on the cutting edge of science: Well maybe just the cutting edge of one small subset of a subset of science, but it still counts.
7. Bullshit Detector: “I swear, my printer broke 2 minutes before that assignment was due!” Yah, I don’t think so.
8. Not afraid to ask for directions: We spend our lives asking questions…this one is easy.
9. We actually care what you have to say: Other opinions count in science.
10. We’ll admit defeat: Scientific progress comes from disproving ideas, not proving them. Show us we’re wrong and we’ll love you for it.

Now that I’m on the dating scene again I’ve been thinking a lot about how the things I’ve learned as a behavioral psychologist apply to the oldest game of them all.

Disclaimer: I wouldn’t say I’m a Casanova, but I do know how to hold my own in the dating arena. Take these suggestions/observations as you wish, but do realize that, much like all the dating advice out there, these are just what work for me.

Changing Venues

If any of you have read, The Game, you know that one of the suggestions Strauss gives is that when on a date, you should always change venues a number of times. This apparently gives the impression that the date has lasted longer and thus that the two of you think know each other more than you actually might. But is there any psychology to this?

It turns out that there is. Our perception of when an event happened in the past is correlated with how many memory markers we have. For example, imagine you were are asked to list all the things you can think of since you last went to an amusement park and also your subjective assessment of when you last went to the amusement park. What research shows is that these your subjective assessment will be correlated with the number of items you listed. Some fancier experimental design has also demonstrated that there is a causal connection here: thinking about events actually leads to the memory change.

So when you’re on date, make sure you go for drinks before and after dinner. With three venues you’ll make it feel like you’ve known the person forever (or at least just a little bit longer)!

Comparability

In Dan Ariely’s recent book, Predictable Irrationality, he makes the point that when going out looking for dates, make sure that the person you bring along is similar to you…but slightly worse on a few dimensions. This is in contrast to bringing your ugly socially awkward friend. Seems strange right? Clearly you’ll look that much better next to the socially awkward friend than to the slightly less attractive one. So why bring the latter?

It turns out that Dan found when we are more likely to make comparisons between objects when those comparisons are easy to make. It’s easy to say “Jim and Steve are pretty similar, but Jim is a little taller…I’ll go with him” than to say “Tom looks like a troll…I don’t even want to think about him…Jim’s okay I guess, but nothing special.”

The point is that you can easily compare two similar people and find the better one, but not so with two dissimilar people. Next you’re out on the town, bring Steve along and let Tom find his own luck!

Leave Them With Something To Remember You By

Let’s assume that you’ve successfully managed to be witty and charming enough to convince someone to go on a second date with you. You’re about to finish your first date with a hug (and maybe a kiss). What should you do? Simple, leave them something to remember you by. Something small and simple works best.

I had a first date where the woman I was with was cold and so I leant her my scarf. This was a cheep $5 scarf I got from some random vendor. If I never saw it again, it wouldn’t be end of the world. But the benefit I got from giving her something was huge.

First, she was no obliged to return the scarf. This meant actually seeing me again, thus increasing the chances that the second date would happen. Second, every time she saw the scarf in her home, she thought of me. Psychology research on fluency suggests that the more one thinks of something, the more one tends to like it (mere-exposure effect…and yes, there is plenty of research on satiation which goes the other way, but when a stimulus (or person) is novel, mere-exposure tends to win out). Third, I now seem like a nice guy who’s willing to lend my things to a person in need.

There is no down side to do doing this, and like I mentioned, plenty of psychology to yield positive potential.

What other dating advice to you all have? Is it backed by anecdotes or by science? I’d love to hear it all!

Continuing on in my How To Lie With Statistics series, let’s talk about “Much Ado About Practically Nothing.”

For the sake of simplicity and consistency we have many standards in this world that involve thresholds. An abortion can only happen prior to the 20th week of gestation, a person is of normal intelligence if his or her IQ is above 100, and a warrant expires after 30 days. We follow these rules as if the cut off period were determined by some all knowing entity and set in stone.

In fact, we’re missing something. That something is the standard error (or standard deviation…I won’t get into the difference right now…but there’s a big one). When we report a threshold, what we’re really reporting is the average time at which something should occur. In the case of abortions, one is termed as “late-term” if 20 weeks have passed. But the reality is that some fetuses mature faster than others and some mature slower than others. So really, we should talk about 20 weeks + or – some number of weeks. We should provide a range of possibilities based on the standard error associated with the underlying data.

Similarly, we can look at the warranty example. Firms know full well how long a product should last and often determine the length of a warranty to be somewhere in that range. What they are ostensibly saying is that their product will last approximately the length of the warranty. But what happens when your laptop break after 1 year and 2 days (assuming a 1 year warranty)? According to the firm, you’re out of luck. But really, the expectation of a 1 year life time is just an average and needs an associated standard error. The life span is really 1 year, plus or minus some number of days/weeks. Firms take advantage of this fact and customers suffer.

The point is that whenever you see an average, make sure to find out the standard error as well. This is done very frequently in political polls when you see that X is beating Y by 3% but the error on the result is also 3%. This suggests that it is very possible that the margin that X is beating Y by doesn’t actually exist in the population. Be aware of this and know you’ll be much better off when reading (and creating) statistics!

Part 1 - The Sample with the Built-in Bias
Part 2- The Well-Chosen Averge
Part 3 - The Little Figures That Are Not There

Often in economics (and certainly in marketing) researchers ask consumers their willingness to pay (WTP) for an item. This can be done following an experimental manipulation to assess the impact of some variable or simply to determine how to price an item. I often use such a question in my own research and generally don’t think much of it.

Recently, however, I had an experience which makes me question the degree to which people truly know how much they are willing to pay for anything. I was bidding for an item on eBay and, being the behavioral scientist that I am, thought about what I should set as my maximum bidding price (I’m far too lazy to snipe). I knew that the item was selling for about $60 in other auctions so I quickly anchored on that value and bid about the same.

Unfortunately, a few hours later I was outbid. At this point, I started asking myself: “well am I really only willing to pay $60 for this item? What about $70?” And the truth was that I had absolutely no clue what I valued this item. From a purely economic point of view, I didn’t know how much utility I would extract from using the item. Would the marginal in crease in cost be offset by the utility I would derive?

There already is a tremendous amount of research on context effects in judgments (i.e. our subjective enjoyment/utility is highly variable and influenced by the environment that we are in), but to what degree is the uncertainty of willingness to pay understood? In auctions we know that people get attached to their item they are bidding on (i.e. endowment effect) and bid more than they should. But this applies a normative standard on how much they “should” bid.

First, I’m suggesting that people have a range that they might be willing to pay for an item rather than an absolute number. This is nothing new of course since we know that variance exists in people’s responses to just about everything. But if we stop here we would say that if we wanted to assess someone’s WTP by using a stepwise elicitation procedure where we ask a person if they would be willing to pay $1, $2, $3, etc… for an item and that would yield a maximum WTP.

However, I think that there’s more to the story. Even after a person has given his/her maximum WTP using this procedure (or any other) they still are uncertain about the utility they will derive from an item and so might adjust up or down based on some contextual cues (endowment in my auction example). If this is true, then price setting is as much of an art form as it is a science.

And please, don’t start on the supply/demand arguments as that will only make me whip out a list of ~500 papers that show how poor the demand side (people) is at figuring out anything.

So what do you think? Do people know what they are willing to pay for items?