What surprised me in 2016

2016 made me reassess an important component of my view of the world. No, not Brexit or Trump becoming President (although, it’s not unrelated).

At the end of 2016, I realized that almost all psychology is pseudo-science. Not hyperbole, not oversold, but pseudo-science.

People used to joke that Parapsychology is the control group for science: i.e., a bunch of people ostentatiously following the scientific method in a situation where every result should come out negative. It’s a null field: the null hypothesis (that there is no effect) is true. Thus, the fact that you can still get positive effects should be worrisome. Turns out the true joke was that psychology is the true control group. Parapsychology was a bad control as most scientists were already predisposed to disbelieve them. Psychology is a much better control.

I had heard of the “Replication Crisis” before, but had not delved into the details. I thought psychology was like microbiome studies: over-hyped but, fundamentally, correct. We may see reports the microbiome makes you be rude to your uber driver or whatever silly effect. We often read about the effects of the microbiome on obesity, as if it didn’t matter that our diets are not as healthy as they should be and it was all down to microbes. Jonathan Eisen collects these as overselling the microbiome. Still, to say that people oversell the microbiome is not to say that there is no effect. The microbes do not single-handedly cause obesity, but they have an impact on the margin (a few BMI points up or down), which is enough to be significant for the population. They may not cause nor cure cancer, but they seem to influence the effect of immunotherapy enough that we may need to adjust dosages/drug combinations. And so on…

I thought that when it came to psychology, the same was true: sure, a lot of hype, but I thought there was a there there. There isn’t.

My basic mistake was that I had shared Daniel Kahneman’s view of the situation:

My position […] was that if a large body of evidence published in reputable journals supports an initially implausible conclusion, then scientific norms require us to believe that conclusion. Implausibility is not sufficient to justify disbelief, and belief in well-supported scientific conclusions is not optional. This position still seems reasonable to me – it is why I think people should believe in climate change.

This was exactly my position until I read this long Andrew Gelman post. Since then, I started to read up on this and find that psychology (as a field) has sold us a bill of goods.

(Computer-programming) language wars a bit silly, but not irrational

I don’t know where I heard it (and it was probably not first hand) the
observation of how weird it is that in the 21st century computer professionals
segregate by the language they use to talk to the machine. It just seems silly, doesn’t it?

Programming language discussions (R vs Python for data science, C++ or Python
for computer vision, Java or C# or Ruby for webapps, …) are a stable of
geekdom and easy to categorize as silly. In this short post, I’ll argue that
that while silly they are not completely irrational.

Programming languages are mostly about tooling

Some languages are better than others, but most of what it matters is not
whether the language itself is any good, but how large the ecosystem around it
is. You can have a perfect language, but if there is no support for it in your
favorite editor/IDE, no good HTTPS libraries which can handle HTTP2.0, then
working in it will be efficient or even less pleasant than working in Java. On
the other hand, PHP is a terrible terrible language, but its ecosystem is (for
its limited domain) very nice. R is a slightly less terrible version of this: not a great language, but a lot of nice libraries and a good culture of documentation.

Haskell is a pretty nice programming language, but working in it got much nicer
once stack appeared on the scene. The
language is the same, even the set of libraries is the same, but having a
better way to install packages is enough to fundamentally change your

On the other hand, Haskell is (still?) enough of a niche language than nobody
has yet written a tool comparable to ccache for
the C/C++ world (instantaneous rebuilds are amazing for a compiled language).

The value of your code increases if you program in a popular language

This is not strictly true: if the work is self-contained, then it may be very
useful on its own even if you wrote it in COBOL, but often the more people can
build upon your work, the more valuable that work is. So if your work is
written in C or Python as opposed to Haskell or Ada, everything else being
equal, it will be more valuable (not everything else is equal, though).

This is somewhat field-dependent. Knowing R is great if you’re a
bioinformatician, but almost useless if you’re writing webserver code. Even
general-purpose languages get niches based on history and tools. Functional
programming languages somehow seems to be more popular in the financial sector
than in other fields (R has a lot of functional elements, but is not typically
thought of as a functional language; probably because functional languages are
“advanced” and R is “for beginners”).

Still, a language that is popular in its field will make your own code more
valuable. Packages upon which you depend will be more likely to be maintained,
tools will improve. If you release a package yourself, it will be more used
(and, if you are in science, maybe even cited).

Changing languages is easy, but costly

Any decent programmer can “pick up” a new language in a few days. I can
probably even debug code in any procedural language even without having ever
seen it before. However, to really become proficient, it often takes much
longer: you need to encounter and internalize the most natural way to do things
in the new language, the quirks of the interpreter/compiler, learn about
different libraries and tools, &c. None of this is “hard”, but it all takes a
long time.

Programming languages have network effects

This is all a different way of saying that programming languages have network
. Thus, if I use language X, it is generally better for me if others
also use it. Not always explicitly, but I think this is the rationale for the programming language discussions.

Utilitarian Scientific Software Development

Yesterday, I added this new feature to ngless: if the user asks it to run a non-existent script, it will try it give an error message with a guess of what you probably really meant.

For example, if you type Profiles.ngl, but the script is actually called profile.ngl:

$ ngless Profiles.ngl

Exiting after fatal error:
File `Profiles.ngl` does not exist. Did you mean 'profile.ngl' (closest match)?

Previously, it would just say Profiles.ngl not found, without a suggestion.

It took me about 10-15 minutes to implement this (actually most of the functionality was already present as ngless already implemented similar functionality in other context). Is it worth it?

When is it worth it to add bells & whistles to research software?

I think we should think about it, in an ideal world, using the utilitarian principle of research software development: software should reduce the overall human effort. If this feature saves more time overall than it took to write, then it’s worth it.

This Utilitarian Principle says that these 15 minutes were well invested if (and only if) this ngless features saves more than 15 minutes for all its users over its lifetime. I expect that every time an user triggers this error, they’ll save a few seconds (say 2 seconds). 15 minutes is 900 seconds. Thus, this feature is worth it if it is triggered over 450 times. Given that we hope that ngless will be widely used, this feature is certainly worth it.

This principle also makes the argument that it would not be worth to add such a feature to a script that is only used in an internal analysis. So, code that was only meant to be used by myself or by myself and a small number of others, should have fewer bells & whistles.

In a non-ideal world, we need to take into account the incentives of the scientific (or commercial) world and the possibility of market failure: the market does not always reward the most selfless behaviour (this includes the “market” for scientific rewards where shiny new things are “paid” more than boring old maintenance).

Scott Sumner on what is a science

And don’t embarrass yourself by arguing macroeconomics is not a science.  Of course it’s a science.  It’s failed science, but then so are some of the other sciences, at least based on what I’ve read about the crisis in replication.  The term ‘science’ is not a compliment, it’s not some sort of award given to a field, like a Nobel Prize.  It’s simply a descriptive term for a field that builds models that try to explain how the world works.  Saying that science must be successful to be viewed as science is as silly as saying that a work of art must be good to be considered art.

Scott Sumner

Psychology is a failed science.

No, computers are not setting us up for disaster

Yesterday, the Guardian published a long essay by Tim Harford on the dangers of automation. The argument is not new (I first heard it on the econtalk episode with David Mindell), and the characteristic example is that of the Air France flight that crashed in the middle of the ocean after the autopilot handed control back to the human pilots who immediately proceeded to crash the plane. As I read it the argument runs as follows: (a) full automation is impossible, (b) partial automation erodes skills, therefore (c) we should be wary of over-automating.

On twitter, I responded with the snark that that medium encourages:

But I feel I should make a longer counter-argument.

1. Despite being a good visual (a plane crash is dramatic), the example of an airplane crash in 2009 is a terrible one. Commercial civil aviation is incredibly safe. Commercial aviation is so safe, I wouldn’t be surprised to read a contrarian Marginal Revolution post arguing it’s now too safe and we need more dangerous planes. I would be very careful in arguing that somehow whatever the aviation community does, is not working based on a single incident that happened 7 years ago. If this was happening every 7 weeks, then it would be a very worrying problem, but it doesn’t.

2. Everything I’ve heard and read about that Air France accident seems to agree that the pilots were deeply incompetent. I have also gotten the distinct impression that if the system had not handed back control to the humans, they would not have crashed the plane. It is simply asserted that we cannot have completely autonomous planes, but without evidence. Perhaps at the very least, it should be harder for the humans to override the automated control. Fully automated planes would also not be hijackable in a 9/11 way nor by their own pilots committing suicide (which given how safe planes are, may now be a significant fraction of airplane deaths!).

3. Even granting the premise of the article, that (a) full automation is impossible and (b) partial automation can lead to skill erosion, the conclusion that “the database and the algorithm, like the autopilot, should be there to support human decision-making” is a non sequitor. It assumes that the human is always a better decision maker, which is completely unproven. In fact, I rather feel that the conclusion is the opposite: the pilot should be there (if a pilot is needed, but let’s grant that) to support the autopilot. Now, we should ask: what’s the best way for pilots to support automated systems? If it is to intervene in times of rare crisis, then pilots should perhaps train like other professionals who are there for crises: a lot of simulations and war games for the cases that we hope never happen. Perhaps, we’ll get to a world where success is measured by having pilots spend their whole careers without ever flying a plane, much like a Secret Service agent trains for the worst, but hopes to never have to throw themselves in front of a bullet.

4. Throughout the essay, it is taken as a given that humans are better and computers are there to save on effort. There is another example, that of meteorologists who now trust the computer instead of being able to intuit when the computer has screwed up, which is what used to happen, but I don’t see an argument that their intuition is better than the computer. If you tell me that the veteran meteorologists can beat the modern systems, I’ll buy that, but I would also think that maybe it’s because the veteran meteorologists were working when the automated systems weren’t as good as the modern ones.

5. The essay as a whole needs to be more quantitative. Even if computers do cause different types of accident, we need to have at least an estimate of whether the number of deaths is larger or smaller than using other systems (humans). I understand that authors do not always choose their titles, but I wouldn’t have responded if title of the essay had been “It won’t be perfect: how automated systems will still have accidents”.

6. The skill erosion effect is interesting per se and there is some value in discussing it and being aware of it. However, I see no evidence that it completely erases the gains from automation (rather than being a small “tax” or clawback on the benefits of automation) and that the solution involves less automation rather than either more automation or a different kind of human training.

7. My horse riding skills are awful.

Scipy’s mannwhitneyu function

Without looking it up, can you say what the following code does:

import numpy as np
from scipy import stats
a = np.arange(25)
b = np.arange(25)+4
print(stats.mannwhitneyu(a , b))

You probably guessed that it computes the Mann-Whitney test between two samples, but exactly which test? The two-sided or the one-sided test?

You can’t tell from the code because it depends on which version of scipy you are running and it has gone back and forth between the two! Pre-0.17.0 it used the one-sided test with the side being decided based on the input data. This was obviously the wrong thing to do. Then, the API was fixed in 0.17.0 to do the two-sided test. This was considered a bad thing because it broke backwards compatibility and now it’s back to performing the one-sided test! I wish I was making this up. 

Reading through the github issues (#4933, #6034,  #6062, #6100)  is an example of how open source projects can stagnate. There is a basic, simple, solution to the issue: create a corrected version of the function with a new name and deprecate the old one. This keeps backwards compatibility while allowing the project to fix its API. Once the issue had been identified, this should have been a 20 minute job. Reading through the issues, this simple solution is proposed, discussed, seemingly agreed to. Instead, something else happens and at this point, it’d take me longer than 20 minutes to just read through the whole discussions.

This is not the first time I have run into numpy/scipy’s lack of respect for backwards compatibility either. Fortunately, there is a solution to this case, which is to use the full version:

stats.mannwhitneyu(a, b, alternative='two-sided')

Repost: BLAST deserves a Nobel Prize

Given that tomorrow (Monday October 3) the 2016 Nobel Prize in Physiology or Medicine will be announced, I am linking to my 2-year old post arguing that BLAST deserves a Nobel Prize:

In terms of impact in the field, it’s undeniable that BLAST has been huge. These people created a verb! What modern biologist does not know what “blasting a sequence” means? The BLAST paper was, at one point, the most highly cited paper in history. The impact on physiology is undeniable.

Lipman and Gene Myers stand out for their contributions to the computational processing of biological sequences. (See how I phrased that in a Nobel Committee way).


[One] counterargument I’ve heard is that BLAST is mostly a method, but so was GFP […] Does anybody believe that just the 1962 discovery of a jellyfish protein would have sufficed for a Nobel?


BLAST was definitely one of the most largest advances in the field of physiology in the last few decades. For this reason, David Lipman and Gene Myers should get a Physiology and Medicine Nobel Prize.

I also add that current favorite CRISPR is also mostly a method (the CRISPR Prize, when it comes, will be awarded for the method not the discovery of some DNA processing mechanism in a Streptococcus species.