## Dos and Don’ts of reduced chi-squared

Posted in Bad Statistics, The Universe and Stuff with tags , , on April 26, 2019 by telescoper

Yesterday I saw a tweet about an arXiv paper and thought I’d share it here. The paper, I mean. It’s not new but I’ve never seen it before and I think it’s well worth reading. The abstract of the paper is:

Reduced chi-squared is a very popular method for model assessment, model comparison, convergence diagnostic, and error estimation in astronomy. In this manuscript, we discuss the pitfalls involved in using reduced chi-squared. There are two independent problems: (a) The number of degrees of freedom can only be estimated for linear models. Concerning nonlinear models, the number of degrees of freedom is unknown, i.e., it is not possible to compute the value of reduced chi-squared. (b) Due to random noise in the data, also the value of reduced chi-squared itself is subject to noise, i.e., the value is uncertain. This uncertainty impairs the usefulness of reduced chi-squared for differentiating between models or assessing convergence of a minimisation procedure. The impact of noise on the value of reduced chi-squared is surprisingly large, in particular for small data sets, which are very common in astrophysical problems. We conclude that reduced chi-squared can only be used with due caution for linear models, whereas it must not be used for nonlinear models at all. Finally, we recommend more sophisticated and reliable methods, which are also applicable to nonlinear models.

I’ve never really understood why this statistic (together with related frequentist-inspired ideas) is treated with such reverence by astronomers, so this paper offers a valuable critique to those tempted to rely on it blindly.

## Bad Statistics and the Gender Gap

Posted in Bad Statistics with tags , , , on April 3, 2019 by telescoper

So there’s an article in Scientific American called How to Close the Gender Gap in the Labo(u)r Force (I’ve added a `u’ to `Labour’ so that it can be understood in the UK).

I was just thinking the other day that it’s been a while since I added any posts to the `Bad Statistics’ folder, but this Scientific American article offers a corker:

That parabola is a  `Regression line’? Seriously? Someone needs to a lesson in how not to over-fit data! It’s plausible that the orange curve might be the best-fitting parabola to the blue points, but that doesn’t mean that it provides a sensible description of the data…

I can see a man walking a dog in the pattern of points to the top right: can I get this observation published in Scientific American?

## Grave Wave Doubts?

Posted in Bad Statistics, The Universe and Stuff with tags , , , , on November 1, 2018 by telescoper

I noticed this morning that this week’s New Scientist cover feature (by Michael Brooks)is entitled Exclusive: Grave doubts over LIGO’s discovery of gravitational waves. The article is behind a paywall – and I’ve so far been unable to locate a hard copy in Maynooth so I haven’t read it yet but it is about the so-called `Danish paper’ that pointed out various unexplained features in LIGO data associated with the first detection of gravitational waves of a binary black hole merger.

I did know this piece was coming, however, as I spoke to the author on the phone some time ago to clarify some points I made in previous blog posts on this issue (e.g. this one and that one). I even ended up being quoted in the article:

Not everyone agrees the Danish choices were wrong. “I think their paper is a good one and it’s a shame that some of the LIGO team have been so churlish in response,” says Peter Coles, a cosmologist at Maynooth University in Ireland.

I stand by that comment, as I think certain members – though by no means all – of the LIGO team have been uncivil in their reaction to the Danish team, implying that they consider it somehow unreasonable that the LIGO results such be subject to independent scrutiny. I am not convinced that the unexplained features in the data released by LIGO really do cast doubt on the detection, but unexplained features there undoubtedly are. Surely it is the job of science to explain the unexplained?

It is an important aspect of the way science works is that when a given individual or group publishes a result, it should be possible for others to reproduce it (or not as the case may be). In normal-sized laboratory physics it suffices to explain the experimental set-up in the published paper in sufficient detail for another individual or group to build an equivalent replica experiment if they want to check the results. In `Big Science’, e.g. with LIGO or the Large Hadron Collider, it is not practically possible for other groups to build their own copy, so the best that can be done is to release the data coming from the experiment. A basic problem with reproducibility obviously arises when this does not happen.

In astrophysics and cosmology, results in scientific papers are often based on very complicated analyses of large data sets. This is also the case for gravitational wave experiments. Fortunately, in astrophysics these days, researchers are generally pretty good at sharing their data, but there are a few exceptions in that field.

Even allowing open access to data doesn’t always solve the reproducibility problem. Often extensive numerical codes are needed to process the measurements and extract meaningful output. Without access to these pipeline codes it is impossible for a third party to check the path from input to output without writing their own version, assuming that there is sufficient information to do that in the first place. That researchers should publish their software as well as their results is quite a controversial suggestion, but I think it’s the best practice for science. In any case there are often intermediate stages between `raw’ data and scientific results, as well as ancillary data products of various kinds. I think these should all be made public. Doing that could well entail a great deal of effort, but I think in the long run that it is worth it.

I’m not saying that scientific collaborations should not have a proprietary period, just that this period should end when a result is announced, and that any such announcement should be accompanied by a release of the data products and software needed to subject the analysis to independent verification.

Given that the detection of gravitational waves is one of the most important breakthroughs ever made in physics, I think this is a matter of considerable regret. I also find it difficult to understand the reasoning that led the LIGO consortium to think it was a good plan only to go part of the way towards open science, by releasing only part of the information needed to reproduce the processing of the LIGO signals and their subsequent statistical analysis. There may be good reasons that I know nothing about, but at the moment it seems to me to me to represent a wasted opportunity.

CLARIFICATION: The LIGO Consortium released data from the first observing run (O1) – you can find it here – early in 2018, but this data set was not available publicly at the time of publication of the first detection, nor when the team from Denmark did their analysis.

I know I’m an extremist when it comes to open science, and there are probably many who disagree with me, so here’s a poll I’ve been running for a year or so on this issue:

Any other comments welcome through the box below!

UPDATE: There is a (brief) response from LIGO (& VIRGO) here.

## Hawking Points in the CMB Sky?

Posted in Astrohype, Bad Statistics, The Universe and Stuff with tags , on October 30, 2018 by telescoper

As I wait in Cardiff Airport for a flight back to civilization, I thought I’d briefly mention a paper that appeared on the arXiv this summer. The abstract of this paper (by Daniel An, Krzysztof A. Meissner and Roger Penrose) reads as follows:

This paper presents powerful observational evidence of anomalous individual points in the very early universe that appear to be sources of vast amounts of energy, revealed as specific signals found in the CMB sky. Though seemingly problematic for cosmic inflation, the existence of such anomalous points is an implication of conformal cyclic cosmology (CCC), as what could be the Hawking points of the theory, these being the effects of the final Hawking evaporation of supermassive black holes in the aeon prior to ours. Although of extremely low temperature at emission, in CCC this radiation is enormously concentrated by the conformal compression of the entire future of the black hole, resulting in a single point at the crossover into our current aeon, with the emission of vast numbers of particles, whose effects we appear to be seeing as the observed anomalous points. Remarkably, the B-mode location found by BICEP 2 is at one of these anomalous points.

The presence of Roger Penrose in the author list of this paper is no doubt a factor that contributed to the substantial amount of hype surrounding it, but although he is the originator of the Conformal Cyclic Cosmology I suspect he didn’t have anything to do with the data analysis presented in the paper as, great mathematician though he is, data analysis is not his forte.

I have to admit that I am very skeptical of the claims made in this paper – as I was in the previous case of claims of a evidence in favour of the Penrose model. In that case the analysis was flawed because it did not properly calculate the probability of the claimed anomalies in the standard model of cosmology. Moreover, the addition of a reference to BICEP2 at the end of the abstract doesn’t strengthen the case. The detection claimed by BICEP2 was (a) in polarization not in temperature and (b) is now known to be consistent with galactic foregrounds.

I will, however, hold my tongue on these claims, at least for the time being. I have an MSc student at Maynooth who is going to try to reproduce the analysis (which is not trivial, as the description in the paper is extremely vague). Watch this space.

## New Polling Agency

Posted in Bad Statistics with tags , , on August 10, 2018 by telescoper

There is a new polling agency on the block, called DeltaPoll.

I had never heard of them until last week, when they had a strange poll published in the Daily Mail (which, obviously, I’m not going to link to).

I think we need new pollsters like we need a hole in the head. These companies are forever misrepresenting the accuracy of their surveys and they confuse more than they inform. I was intrigued, however, so I looked up their Twitter profile and found this:

They don’t have a big Twitter following, but the names behind it have previously been associated with other polling agencies, so perhaps it’s not as dodgy as I assumed.

On the other hand, what on Earth does ’emotional and mathematical measurement methods’ mean?

## The Problem with Odd Moments

Posted in Bad Statistics, Cute Problems, mathematics with tags , , on July 9, 2018 by telescoper

Last week, realizing that it had been a while since I posted anything in the cute problems folder, I did a quick post before going to a meeting. Unfortunately, as a couple of people pointed out almost immediately, there was a problem with the question (a typo in the form of a misplaced bracket). I took the post offline until I could correct it and then promptly forgot about it. I remembered it yesterday so have now corrected it. I also added a useful integral as a hint at the end, because I’m a nice person. I suggest you start by evaluating the expectation value (i.e. the first-order moment). Answers to parts (2) and (3) through the comments box please!

SOLUTION: I’ll leave you to draw your own sketch but, as Anton correctly points out, this is a distribution that is asymmetric about its mean but has all odd-order moments equal (including the skewness) equal to zero. it therefore provides a counter-example to common assertions, e.g. that asymmetric distributions must have non-zero skewness. The function shown in the problem was originally given by Stieltjes, but a general discussion can be be found in E. Churchill (1946) Information given by odd moments, Ann. Math. Statist. 17, 244-6. The paper is available online here.

## Literary Bayesianism

Posted in Bad Statistics with tags , on July 3, 2018 by telescoper

I’m a bit busy today doing job interviews and other things, so I’ve just got time for a quick post to point out that there’s a nice polemical piece by David Papineau in the online version of the Times Literary Supplement recently called Thomas Bayes and the crisis in science. I get the print version of the TLS every week, largely for the crossword, but I think the online version of Papineau’s piece is public (i.e. there’s no paywall).

The piece touches on a number of themes I’ve covered on this blog over the years, in particular the widespread use of dodgy statistical methods in science. Here’s a little taster:

One of the great scandals of modern intellectual life is the way generations of statistics students have been indoctrinated into the farrago of significance testing.

I couldn’t agree more!

## Hubble Constant Catch-Up

Posted in Bad Statistics, The Universe and Stuff with tags , , , , on May 2, 2018 by telescoper

Last week when I wrote about the 2nd Data Release from Gaia, somebody emailed me to ask whether the new results said anything about the cosmological distance ladder and hence the Hubble Constant. As far as I could see, no scientific papers were released on this topic at the time and I thought there probably wasn’t anything definitive at this stage. However, it turns out that there is a paper now, by Riess et al., which focuses on the likely impact of Gaia on the Cepheid distance scale. Here is the abstract:

We present HST photometry of a selected sample of 50 long-period, low-extinction Milky Way Cepheids measured on the same WFC3 F555W, F814W, and F160W-band photometric system as extragalactic Cepheids in SN Ia hosts. These bright Cepheids were observed with the WFC3 spatial scanning mode in the optical and near-infrared to mitigate saturation and reduce pixel-to-pixel calibration errors to reach a mean photometric error of 5 millimags per observation. We use the new Gaia DR2 parallaxes and HST photometry to simultaneously constrain the cosmic distance scale and to measure the DR2 parallax zeropoint offset appropriate for Cepheids. We find a value for the zeropoint offset of -46 +/- 13 muas or +/- 6 muas for a fixed distance scale, higher than found from quasars, as expected, for these brighter and redder sources. The precision of the distance scale from DR2 has been reduced by a factor of 2.5 due to the need to independently determine the parallax offset. The best fit distance scale is 1.006 +/- 0.033, relative to the scale from Riess et al 2016 with H0=73.24 km/s/Mpc used to predict the parallaxes photometrically, and is inconsistent with the scale needed to match the Planck 2016 CMB data combined with LCDM at the 2.9 sigma confidence level (99.6%). At 96.5% confidence we find that the formal DR2 errors may be underestimated as indicated. We identify additional error associated with the use of augmented Cepheid samples utilizing ground-based photometry and discuss their likely origins. Including the DR2 parallaxes with all prior distance ladder data raises the current tension between the late and early Universe route to the Hubble constant to 3.8 sigma (99.99 %). With the final expected precision from Gaia, the sample of 50 Cepheids with HST photometry will limit to 0.5% the contribution of the first rung of the distance ladder to the uncertainty in the Hubble constant.

So, nothing definitive yet but potentially very interesting in the future and this group, led by Adam Riess, is now claiming a 3.8σ tension between measurements of the Hubble constant from cosmic microwave background measurements and from traditional `distance ladder’ approaches, though to my mind this is based on some rather subjective judgements.

The appearance of that paper reminded me that I forgot to post about a paper by Bernal & Peacock that appeared a couple of months ago. Here is the abstract of that one:

When combining data sets to perform parameter inference, the results will be unreliable if there are unknown systematics in data or models. Here we introduce a flexible methodology, BACCUS: BAyesian Conservative Constraints and Unknown Systematics, which deals in a conservative way with the problem of data combination, for any degree of tension between experiments. We introduce hyperparameters that describe a bias in each model parameter for each class of experiments. A conservative posterior for the model parameters is then obtained by marginalization both over these unknown shifts and over the width of their prior. We contrast this approach with an existing hyperparameter method in which each individual likelihood is scaled, comparing the performance of each approach and their combination in application to some idealized models. Using only these rescaling hyperparameters is not a suitable approach for the current observational situation, in which internal null tests of the errors are passed, and yet different experiments prefer models that are in poor agreement. The possible existence of large shift systematics cannot be constrained with a small number of data sets, leading to extended tails on the conservative posterior distributions. We illustrate our method with the case of the H0 tension between results from the cosmic distance ladder and physical measurements that rely on the standard cosmological model.

This paper addresses the long-running issue of apparent tension in different measurements of the Hubble constant that I’ve blogged about before (e.g. here) by putting the treatment of possible systematic errors into a more rigorus and consistent (i.e. Bayesian) form. It says what I think most people in the community privately think about this issue, i.e. that it’s probably down to some sort of unidentified systematic rather than exotic physics.

The title of the paper includes the phrase `Conservative Cosmology’, but I think that’s a bit of a misnomer. I think `Sensible Cosmology’. Current events suggest `conservative’ and `sensible’ have opposite meanings. You can find a popular account of it here, from which I have stolen this illustration of the tension:

A chart showing the two differing results for the Hubble constant – The expansion rate of the universe (in km/s/Mpc)
Result 1: 67.8 ± 0.9 Cosmic microwave background
Result 2: 73.52 ± 1.62 Cosmic distance ladder

Anyway, I have a poll that has been going on for some time about whether this tension is anything to be excited about, so why not use this opportunity cast your vote?

## The Dark Matter of Astronomy Hype

Posted in Astrohype, Bad Statistics, The Universe and Stuff with tags , , , , on April 16, 2018 by telescoper

Just before Easter (and, perhaps more significantly, just before April Fool’s Day) a paper by van Dokkum et al. was published in Nature with the title A Galaxy Lacking Dark Matter. As is often the case with scientific publications presented in Nature, the press machine kicked into action and stories about this mysterious galaxy appeared in print and online all round the world.

So what was the result? Here’s the abstract of the Nature paper:

Studies of galaxy surveys in the context of the cold dark matter paradigm have shown that the mass of the dark matter halo and the total stellar mass are coupled through a function that varies smoothly with mass. Their average ratio Mhalo/Mstars has a minimum of about 30 for galaxies with stellar masses near that of the Milky Way (approximately 5 × 1010 solar masses) and increases both towards lower masses and towards higher masses. The scatter in this relation is not well known; it is generally thought to be less than a factor of two for massive galaxies but much larger for dwarf galaxies. Here we report the radial velocities of ten luminous globular-cluster-like objects in the ultra-diffuse galaxy NGC1052–DF2, which has a stellar mass of approximately 2 × 108 solar masses. We infer that its velocity dispersion is less than 10.5 kilometres per second with 90 per cent confidence, and we determine from this that its total mass within a radius of 7.6 kiloparsecs is less than 3.4 × 108 solar masses. This implies that the ratio Mhalo/Mstars is of order unity (and consistent with zero), a factor of at least 400 lower than expected. NGC1052–DF2 demonstrates that dark matter is not always coupled with baryonic matter on galactic scales.

I had a quick look at the paper at the time and wasn’t very impressed by the quality of the data. To see why look at the main plot, a histogram formed from just ten observations (of globular clusters used as velocity tracers):

I didn’t have time to read the paper thoroughly before the Easter weekend,  but did draft a sceptical blog on the paper only to decide not to publish it as I thought it might be too inflammatory even by my standards! Suffice to say that I was unconvinced.

Anyway, it turns out I was far from the only astrophysicist to have doubts about this result; you can find a nice summary of the discussion on social media here and here. Fortunately, people more expert than me have found the time to look in more detail at the Dokkum et al. claim. There’s now a paper on the arXiv by Martin et al.

It was recently proposed that the globular cluster system of the very low surface-brightness galaxy NGC1052-DF2 is dynamically very cold, leading to the conclusion that this dwarf galaxy has little or no dark matter. Here, we show that a robust statistical measure of the velocity dispersion of the tracer globular clusters implies a mundane velocity dispersion and a poorly constrained mass-to-light ratio. Models that include the possibility that some of the tracers are field contaminants do not yield a more constraining inference. We derive only a weak constraint on the mass-to-light ratio of the system within the half-light radius or within the radius of the furthest tracer (M/L_V<8.1 at the 90-percent confidence level). Typical mass-to-light ratios measured for dwarf galaxies of the same stellar mass as NGC1052-DF2 are well within this limit. With this study, we emphasize the need to properly account for measurement uncertainties and to stay as close as possible to the data when determining dynamical masses from very small data sets of tracers.

Whatever turns out in the final analysis of NGC1052-DF2 it is undoubtedly an interesting system. It may indeed turn out to  have less dark matter than expected though I don’t think the evidence available right now warrants such an inference with such confidence. What worries me most however, is the way this result was presented in the media, with virtually no regard for the manifest statistical uncertainty inherent in the analysis. This kind of hype can be extremely damaging to science in general, and to explain why I’ll go off on a rant that I’ve indulged in a few times before on this blog.

A few years ago there was an interesting paper  (in Nature of all places), the opening paragraph of which reads:

The past few years have seen a slew of announcements of major discoveries in particle astrophysics and cosmology. The list includes faster-than-light neutrinos; dark-matter particles producing γ-rays; X-rays scattering off nuclei underground; and even evidence in the cosmic microwave background for gravitational waves caused by the rapid inflation of the early Universe. Most of these turned out to be false alarms; and in my view, that is the probable fate of the rest.

The piece went on to berate physicists for being too trigger-happy in claiming discoveries, the BICEP2 fiasco being a prime example. I agree that this is a problem, but it goes far beyond physics. In fact its endemic throughout science. A major cause of it is abuse of statistical reasoning.

Anyway, I thought I’d take the opportunity to re-iterate why I statistics and statistical reasoning are so important to science. In fact, I think they lie at the very core of the scientific method, although I am still surprised how few practising scientists are comfortable with even basic statistical language. A more important problem is the popular impression that science is about facts and absolute truths. It isn’t. It’s a <em>process</em>. In order to advance it has to question itself. Getting this message wrong – whether by error or on purpose -is immensely dangerous.

Statistical reasoning also applies to many facets of everyday life, including business, commerce, transport, the media, and politics. Probability even plays a role in personal relationships, though mostly at a subconscious level. It is a feature of everyday life that science and technology are deeply embedded in every aspect of what we do each day. Science has given us greater levels of comfort, better health care, and a plethora of labour-saving devices. It has also given us unprecedented ability to destroy the environment and each other, whether through accident or design.

Civilized societies face rigorous challenges in this century. We must confront the threat of climate change and forthcoming energy crises. We must find better ways of resolving conflicts peacefully lest nuclear or chemical or even conventional weapons lead us to global catastrophe. We must stop large-scale pollution or systematic destruction of the biosphere that nurtures us. And we must do all of these things without abandoning the many positive things that science has brought us. Abandoning science and rationality by retreating into religious or political fundamentalism would be a catastrophe for humanity.

Unfortunately, recent decades have seen a wholesale breakdown of trust between scientists and the public at large. This is due partly to the deliberate abuse of science for immoral purposes, and partly to the sheer carelessness with which various agencies have exploited scientific discoveries without proper evaluation of the risks involved. The abuse of statistical arguments have undoubtedly contributed to the suspicion with which many individuals view science.

There is an increasing alienation between scientists and the general public. Many fewer students enrol for courses in physics and chemistry than a a few decades ago. Fewer graduates mean fewer qualified science teachers in schools. This is a vicious cycle that threatens our future. It must be broken.

The danger is that the decreasing level of understanding of science in society means that knowledge (as well as its consequent power) becomes concentrated in the minds of a few individuals. This could have dire consequences for the future of our democracy. Even as things stand now, very few Members of Parliament are scientifically literate. How can we expect to control the application of science when the necessary understanding rests with an unelected “priesthood” that is hardly understood by, or represented in, our democratic institutions?

Very few journalists or television producers know enough about science to report sensibly on the latest discoveries or controversies. As a result, important matters that the public needs to know about do not appear at all in the media, or if they do it is in such a garbled fashion that they do more harm than good.

Years ago I used to listen to radio interviews with scientists on the Today programme on BBC Radio 4. I even did such an interview once. It is a deeply frustrating experience. The scientist usually starts by explaining what the discovery is about in the way a scientist should, with careful statements of what is assumed, how the data is interpreted, and what other possible interpretations might be and the likely sources of error. The interviewer then loses patience and asks for a yes or no answer. The scientist tries to continue, but is badgered. Either the interview ends as a row, or the scientist ends up stating a grossly oversimplified version of the story.

Some scientists offer the oversimplified version at the outset, of course, and these are the ones that contribute to the image of scientists as priests. Such individuals often believe in their theories in exactly the same way that some people believe religiously. Not with the conditional and possibly temporary belief that characterizes the scientific method, but with the unquestioning fervour of an unthinking zealot. This approach may pay off for the individual in the short term, in popular esteem and media recognition – but when it goes wrong it is science as a whole that suffers. When a result that has been proclaimed certain is later shown to be false, the result is widespread disillusionment.

The worst example of this tendency that I can think of is the constant use of the phrase “Mind of God” by theoretical physicists to describe fundamental theories. This is not only meaningless but also damaging. As scientists we should know better than to use it. Our theories do not represent absolute truths: they are just the best we can do with the available data and the limited powers of the human mind. We believe in our theories, but only to the extent that we need to accept working hypotheses in order to make progress. Our approach is pragmatic rather than idealistic. We should be humble and avoid making extravagant claims that can’t be justified either theoretically or experimentally.

The more that people get used to the image of “scientist as priest” the more dissatisfied they are with real science. Most of the questions asked of scientists simply can’t be answered with “yes” or “no”. This leaves many with the impression that science is very vague and subjective. The public also tend to lose faith in science when it is unable to come up with quick answers. Science is a process, a way of looking at problems not a list of ready-made answers to impossible problems. Of course it is sometimes vague, but I think it is vague in a rational way and that’s what makes it worthwhile. It is also the reason why science has led to so many objectively measurable advances in our understanding of the World.

I don’t have any easy answers to the question of how to cure this malaise, but do have a few suggestions. It would be easy for a scientist such as myself to blame everything on the media and the education system, but in fact I think the responsibility lies mainly with ourselves. We are usually so obsessed with our own research, and the need to publish specialist papers by the lorry-load in order to advance our own careers that we usually spend very little time explaining what we do to the public or why.

I think every working scientist in the country should be required to spend at least 10% of their time working in schools or with the general media on “outreach”, including writing blogs like this. People in my field – astronomers and cosmologists – do this quite a lot, but these are areas where the public has some empathy with what we do. If only biologists, chemists, nuclear physicists and the rest were viewed in such a friendly light. Doing this sort of thing is not easy, especially when it comes to saying something on the radio that the interviewer does not want to hear. Media training for scientists has been a welcome recent innovation for some branches of science, but most of my colleagues have never had any help at all in this direction.

The second thing that must be done is to improve the dire state of science education in schools. Over the last two decades the national curriculum for British schools has been dumbed down to the point of absurdity. Pupils that leave school at 18 having taken “Advanced Level” physics do so with no useful knowledge of physics at all, even if they have obtained the highest grade. I do not at all blame the students for this; they can only do what they are asked to do. It’s all the fault of the educationalists, who have done the best they can for a long time to convince our young people that science is too hard for them. Science can be difficult, of course, and not everyone will be able to make a career out of it. But that doesn’t mean that it should not be taught properly to those that can take it in. If some students find it is not for them, then so be it. We don’t everyone to be a scientist, but we do need many more people to understand how science really works.

I realise I must sound very gloomy about this, but I do think there are good prospects that the gap between science and society may gradually be healed. The fact that the public distrust scientists leads many of them to question us, which is a very good thing. They should question us and we should be prepared to answer them. If they ask us why, we should be prepared to give reasons. If enough scientists engage in this process then what will emerge is and understanding of the enduring value of science. I don’t just mean through the DVD players and computer games science has given us, but through its cultural impact. It is part of human nature to question our place in the Universe, so science is part of what we are. It gives us purpose. But it also shows us a way of living our lives. Except for a few individuals, the scientific community is tolerant, open, internationally-minded, and imbued with a philosophy of cooperation. It values reason and looks to the future rather than the past. Like anyone else, scientists will always make mistakes, but we can always learn from them. The logic of science may not be infallible, but it’s probably the best logic there is in a world so filled with uncertainty.

## What happens if you ask people to pick a number `at random’ between 1 and 100?

Posted in Bad Statistics with tags on April 11, 2018 by telescoper

I saw this circulating on Twitter and thought I would share it here; it was originally posted on reddit.

The graph shows the results obtained when 6750 people were asked to pick an integer `at random’ between 1 and 100. You might naively expect the histogram to be flat (give or take some Poisson errors), consistent with each number having the same probability of being picked, but there are clearly some numbers that are more likely to be chosen than a constant probability would imply. The most popular picks are in fact 69, 77 and 7 (in descending order).

It’s well known amongst purveyors of conjuring tricks and the like that if you ask people to pick a number between 1 and 10, far more people choose 7 than any other number. And I suppose 77 is an extension of that. More interestingly, however, the top result implies that, given the choice, more people seem to prefer a 69 to anything else…

Anyway, it proves a point that I’ve made more than a few times on this blog, namely that people generally have a very poor idea of what randomness is and are particularly bad at making random choices or generating random sequences.

P.S. Please direct any criticism of the graph (e.g. why the x-axis goes up to 104 or why the x-values are given to two decimal places) to the reddit page…