How to read and understand a scientific paper: a guide for non-scientists

Before you begin: some general advice
Reading a scientific paper is a completely different process than reading an article about science in a blog or newspaper. Not only do you read the sections in a different order than they’re presented, but you also have to take notes, read it multiple times, and probably go look up other papers for some of the details. Reading a single paper may take you a very long time at first. Be patient with yourself. The process will go much faster as you gain experience.

http://violentmetaphors.com/2013/08/25/how-to-read-and-understand-a-scientific-paper-2/

Use the "Triple Nod" in Interviews

The triple nod is the non-verbal equivalent of the ellipses. It is a nonverbal cue for someone to keep talking. If you are introverted and aren't great at making conversations, you want to encourage the person you are speaking with to keep talking. Once they are done speaking and pause, nod three times in quick succession and they will often continue. If not, you can pick up where the conversation left off, but this is a great way of showing engagement and lengthening a discussion.

Publishers withdraw more than 120 gibberish papers by Richard Van Noorden

Conference proceedings removed from subscription databases after scientist reveals that they were computer-generated.

The publishers Springer and IEEE are removing more than 120 papers from their subscription services after a French researcher discovered that the works were computer-generated nonsense.

Over the past two years, computer scientist Cyril Labbé of Joseph Fourier University in Grenoble, France, has catalogued computer-generated papers that made it into more than 30 published conference proceedings between 2008 and 2013. Sixteen appeared in publications by Springer, which is headquartered in Heidelberg, Germany, and more than 100 were published by the Institute of Electrical and Electronic Engineers (IEEE), based in New York. Both publishers, which were privately informed by Labbé, say that they are now removing the papers.

Among the works were, for example, a paper published as a proceeding from the 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, held in Chengdu, China. (The conference website says that all manuscripts are “reviewed for merits and contents”.) The authors of the paper, entitled ‘TIC: a methodology for the construction of e-commerce’, write in the abstract that they “concentrate our efforts on disproving that spreadsheets can be made knowledge-based, empathic, and compact”. (Nature News has attempted to contact the conference organizers and named authors of the paper but received no reply*; however at least some of the names belong to real people. The IEEE has now removed the paper).

*Update: One of the named authors replied to Nature News on 25 February. He said that he first learned of the article when conference organizers notified his university in December 2013; and that he does not know why he was a listed co-author on the paper. "The matter is being looked into by the related investigators," he said. 

 

How to create a nonsense paper

Labbé developed a way to automatically detect manuscripts composed by a piece of software called SCIgen, which randomly combines strings of words to produce fake computer-science papers. SCIgen was invented in 2005 by researchers at the Massachusetts Institute of Technology (MIT) in Cambridge to prove that conferences would accept meaningless papers — and, as they put it, “to maximize amusement” (see ‘Computer conference welcomes gobbledegook paper’). A related program generates random physics manuscript titles on the satirical website arXiv vs. snarXiv. SCIgen is free to download and use, and it is unclear how many people have done so, or for what purposes. SCIgen’s output has occasionally popped up at conferences, when researchers have submitted nonsense papers and then revealed the trick.

Labbé does not know why the papers were submitted — or even if the authors were aware of them. Most of the conferences took place in China, and most of the fake papers have authors with Chinese affiliations. Labbé has emailed editors and authors named in many of the papers and related conferences but received scant replies; one editor said that he did not work as a program chair at a particular conference, even though he was named as doing so, and another author claimed his paper was submitted on purpose to test out a conference, but did not respond on follow-up. Nature has not heard anything from a few enquiries.

“I wasn’t aware of the scale of the problem, but I knew it definitely happens. We do get occasional e-mails from good citizens letting us know where SCIgen papers show up,” says Jeremy Stribling, who co-wrote SCIgen when he was at MIT and now works at VMware, a software company in Palo Alto, California.
“The papers are quite easy to spot,” says Labbé, who has built a website where users can test whether papers have been created using SCIgen. His detection technique, described in a study¹ published in Scientometrics in 2012, involves searching for characteristic vocabulary generated by SCIgen. Shortly before that paper was published, Labbé informed the IEEE of 85 fake papers he had found. Monika Stickel, director of corporate communications at IEEE, says that the publisher “took immediate action to remove the papers” and “refined our processes to prevent papers not meeting our standards from being published in the future”. In December 2013, Labbé informed the IEEE of another batch of apparent SCIgen articles he had found. Last week, those were also taken down, but the web pages for the removed articles give no explanation for their absence.
Ruth Francis, UK head of communications at Springer, says that the company has contacted editors, and is trying to contact authors, about the issues surrounding the articles that are coming down. The relevant conference proceedings were peer reviewed, she confirms — making it more mystifying that the papers were accepted.
The IEEE would not say, however, whether it had contacted the authors or editors of the suspected SCIgen papers, or whether submissions for the relevant conferences were supposed to be peer reviewed. “We continue to follow strict governance guidelines for evaluating IEEE conferences and publications,” Stickel said.

 

A long history of fakes

Labbé is no stranger to fake studies. In April 2010, he used SCIgen to generate 102 fake papers by a fictional author called Ike Antkare [see pdf]. Labbé showed how easy it was to add these fake papers to the Google Scholar database, boosting Ike Antkare’s h-index, a measure of published output, to 94 — at the time, making Antkare the world's 21st most highly cited scientist. Last year, researchers at the University of Granada, Spain, added to Labbé’s work, boosting their own citation scores in Google Scholar by uploading six fake papers with long lists to their own previous work².

Labbé says that the latest discovery is merely one symptom of a “spamming war started at the heart of science” in which researchers feel pressured to rush out papers to publish as much as possible.
There is a long history of journalists and researchers getting spoof papers accepted in conferences or by journals to reveal weaknesses in academic quality controls — from a fake paper published by physicist Alan Sokal of New York University in the journal Social Text in 1996, to a sting operation by US reporter John Bohannon published in Science in 2013, in which he got more than 150 open-access journals to accept a deliberately flawed study for publication.

Labbé emphasizes that the nonsense computer science papers all appeared in subscription offerings. In his view, there is little evidence that open-access publishers — which charge fees to publish manuscripts — necessarily have less stringent peer review than subscription publishers.

Labbé adds that the nonsense papers were easy to detect using his tools, much like the plagiarism checkers that many publishers already employ. But because he could not automatically download all papers from the subscription databases, he cannot be sure that he has spotted every SCIgen-generated paper.

http://www.nature.com/news/publishers-withdraw-more-than-120-gibberish-papers-1.14763

The changing face of psychology

 

 Psychology is championing important changes to culture and practice, including a greater emphasis on transparency, reliability, and adherence to the scientific method. Photograph: Sebastian Kaulitzki/Alamy

http://www.theguardian.com/science/head-quarters/2014/jan/24/the-changing-face-of-psychology

After 50 years of stagnation in research practices, psychology is leading reforms that will benefit all life sciences.

In 1959, an American researcher named Ted Sterling reported something disturbing. Of 294 articles published across four major psychology journals, 286 had reported positive results – that is, a staggering 97% of published papers were underpinned by statistically significant effects. Where, he wondered, were all the negative results – the less exciting or less conclusive findings? Sterling labelled this publication bias a form of malpractice. After all, getting published in science should never depend on getting the “right results”.

You might think that Sterling’s discovery would have led the psychologists of 1959 to sit up and take notice. Groups would be assembled to combat the problem, ensuring that the scientific record reflected a balanced sum of the evidence. Journal policies would be changed, incentives realigned.

Sadly, that never happened. Thirty-six years later, in 1995, Sterling took another look at the literature and found exactly the same problem – negative results were still being censored. Fifteen years after that, Daniele Fanelli from the University of Edinburgh confirmed it yet again. Publication bias had turned out to be the ultimate bad car smell, a prime example of how irrational research practices can linger on and on.

Now, finally, the tide is turning. A growing number of psychologists – particularly the younger generation – are fed up with results that don’t replicate, journals that value story-telling over truth, and an academic culture in which researchers treat data as their personal property. Psychologists are realising that major scientific advances will require us to stamp out malpractice, face our own weaknesses, and overcome the ego-driven ideals that maintain the status quo.
Here are five key developments to watch in 2014.
  
1. Replication

The problem: The best evidence for a genuine discovery is showing that independent scientists can replicate it using the same method. If it replicates repeatedly then we can use it to build better theories. If it doesn't then it belongs in the trash bin of history. This simple logic underpins all science – without replication we’d still believe in phlogiston and faster-than-light neutrinos.

In psychology, attempts to closely reproduce previous methods are rarely attempted. Psychologists tend to see such work as boring, lacking in intellectual prowess, and a waste of limited resources. Some of the most prominent psychology journals even have explicit policies against publishing replications, instead offering readers a diet of fast food: results that are novel, eye catching, and even counter-intuitive. Exciting results are fine provided they replicate. The problem is that nobody bothers to try, which litters the field with results of unknown (likely low) value.

How it’s changing: The new generation of psychologists understands that independent replication is crucial for real advancement and to earn wider credibility in science. A beautiful example of this drive is the Many Labs project led by Brian Nosek from the University of Virginia. Nosek and a team of 50 colleagues located in 36 labs worldwide sought to replicate 13 key findings in psychology, across a sample of 6,344 participants. Ten of the effects replicated successfully.

Journals are also beginning to respect the importance of replication. The prominent outlet Perspectives on Psychological Science recently launched an initiative that specifically publishes direct replications of previous studies. Meanwhile, journals such as BMC Psychology and PLOS ONE officially disown the requirement for researchers to report novel, positive findings.

2. Open Access 

The problem: Strictly speaking, most psychology research isn’t really “published” – it is printed within journals that expressly deny access to the public (unless you are willing to pay for a personal subscription or spend £30+ on a single article). Some might say this is no different to traditional book publishing, so what's the problem? But remember that the public being denied access to science is the very same public that already funds most psychology research, including the subscription fees for universities. So why, you might ask, is taxpayer-funded research invisible to the taxpayers that funded it? The answer is complicated enough to fill a 140-page government report, but the short version is that the government places the business interests of corporate publishers ahead of the public interest in accessing science.
 
How it’s changing: The open access movement is growing in size and influence. Since April 2013, all research funded by UK research councils, including psychology, must now be fully open access – freely viewable to the public. Charities such as the Wellcome Trust have similar policies. These moves help alleviate the symptoms of closed access but don’t address the root cause, which is market dominance by traditional subscription publishers. Rather than requiring journals to make articles publicly available, the research councils and charities are merely subsidising those publishers, in some cases paying them extra for open access on top of their existing subscription fees. What other business in society is paid twice for a product that it didn’t produce in the first place? It remains a mystery who, other than the publishers themselves, would call this bizarre set of circumstances a “solution”.

3. Open Science

The problem: Data sharing is crucial for science but rare in psychology. Even though ethical guidelines require authors to share data when requested, such requests are usually ignored or denied, even when coming from other psychologists. Failing to publicly share data makes it harder to do meta-analysis and easier for unscrupulous researchers to get away with fraud. The most serious fraud cases, such as Diederik Stapel, would have been caught years earlier if journals required the raw data to be published alongside research articles.

How it’s changing: Data sharing isn’t yet mandatory, but it is gradually becoming unacceptable for psychologists not to share. Evidence shows that studies which share data tend to be more accurate and less likely to make statistical errors. Public repositories such as Figshare and the Open Science Framework now make the act of sharing easy, and new journals including the Journal of Open Psychology Data have been launched specifically to provide authors with a way of publicising data sharing.

Some existing journals are also introducing rewards to encourage data sharing. Since 2014, authors who share data at the journal Psychological Science will earn an Open Data badge, printed at the top of the article. Coordinated data sharing carries all kinds of other benefits too – for instance, it allows future researchers to run meta-analysis on huge volumes of existing data, answering questions that simply can’t be tackled with smaller datasets.

4. Bigger Data

The problem: We’ve known for decades that psychology research is statistically underpowered. What this means is that even when genuine phenomena exist, most experiments don’t have sufficiently large samples to detect them. The curse of low power cuts both ways: not only is an underpowered experiment likely to miss finding water in the desert, it’s also more likely to lead us to a mirage.

How it’s changing: Psychologists are beginning to develop innovative ways to acquire larger samples. An exciting approach is Internet testing, which enables easy data collection from thousands of participants. One recent study managed to replicate 10 major effects in psychology using Amazon’s Mechanical Turk. Psychologists are also starting to work alongside organisations that already collect large amounts of useful data (and no, I don’t mean GCHQ). A great example is collaborative research with online gaming companies. Tom Stafford from the University of Sheffield recently published an extraordinary study of learning patterns in over 850,000 people by working with a game developer.

5. Limiting Researcher "Degrees of Freedom"

The problem: In psychology, discoveries tend to be statistical. This means that to test a particular hypothesis, say, about motor actions, we might measure the difference in reaction times or response accuracy between two experimental conditions. Because the measurements contain noise (or “unexplained variability”), we rely on statistical tests to provide us with a level of certainty in the outcome. This is different to other sciences where discoveries are more black and white, like finding a new rock layer or observing a supernova.

Whenever experiments rely on inferences from statistics, researchers can exploit “degrees of freedom” in the analyses to produce desirable outcomes. This might involve trying different ways of removing statistical outliers or the effect of different statistical models, and then only reporting the approach that “worked” best in producing attractive results. Just as buying all the tickets in a raffle guarantees a win, exploiting researcher degrees of freedom can guarantee a false discovery.

The reason we fall into this trap is because of incentives and human nature. As Sterling showed in 1959, psychology journals select which studies to publish not based on the methods but on the results: getting published in the most prominent, career-making journals requires researchers to obtain novel, positive, statistically significant effects. And because statistical significance is an arbitrary threshold (p<.05), researchers have every incentive to tweak their analyses until the results cross the line. These behaviours are common in psychology – a recent survey led by Leslie John from Harvard University estimated that at least 60% of psychologists selectively report analyses that “work”. In many cases such behaviour may even be unconscious.

How it’s changing: The best cure for researcher degrees of freedom is to pre-register the predictions and planned analyses of experiments before looking at the data. This approach is standard practice in medicine because it helps prevent the desires of the researcher from influencing the outcome. Among the basic life sciences, psychology is now leading the way in advancing pre-registration. The journals Cortex, Attention Perception & Psychophysics, AIMS Neuroscience and Experimental Psychology offer pre-registered articles in which peer review happens before experiments are conducted. Not only does pre-registration put the reins on researcher degrees of freedom, it also prevents journals from selecting which papers to publish based on the results.

Journals aren’t the only organisations embracing pre-registration. The Open Science Framework invites psychologists to publish their protocols, and the 2013 Declaration of Helsinki now requires public pre-registration of all human research “before recruitment of the first subject”.

Feynman on Scientific Method

Now I'm going to discuss how we would look for a new law. In general, we look for a new law by the following process: first, we guess it, no, don’t laugh, that’s the truth. Then we compute the consequences of the guess, to see what, if this is right, if this law we guessed is right, to see what it would imply and then we compare the computation results to nature or we say compare to experiment or experience, compare it directly with observations to see if it works.

If it disagrees with experiment, it’s wrong! In that simple statement is the key to science. It doesn’t make any difference how beautiful your guess is, it doesn’t make a difference how smart you are, who made the guess, or what his name is… If it disagrees with experiment, it’s wrong. That’s all there is to it.

It is therefore not unscientific to make a guess, although many people who are not in science think it is. For instance, I had a conversation about flying saucers, some years ago, with a layman — because I am scientific I know all about flying saucers! I said “I don’t think there are flying saucers”. So the other...my antagonist said, “Is it impossible that there are flying saucers? Can you prove that it’s impossible?” “No, I can’t prove it’s impossible. It’s just very unlikely”. At that he said, “You are very unscientific. If you can’t prove it impossible then how can you say that it’s unlikely?” But that is the way that is scientific. It is scientific only to say what is more likely and what less likely, and not to be proving all the time the possible and impossible. To define what I mean, I might have said to him, "Listen, I mean that from my knowledge of the world that I see around me, I think that it is much more likely that the reports of flying saucers are the results of the known irrational characteristics of terrestrial intelligence than of the unknown rational efforts of extra-terrestrial intelligence." It is just more likely. That is all, and it is a very good guess. And we always try to guess the most likely explanation, keeping in the back of our minds the fact that if it does not work, then we must discuss the other possiblities.

There was, for instance, for a while, a phenomenon called super-conductivity, there still is the phenomenon, which is that metals conduct electricity without resistance at low temperatures and it was not at first obvious that this was a consequence of the known laws with these particles. Now that it has been thought through carefully enough, it is seen in fact to be fully explainable in terms of our present knowledge.

There are other phenomena, such as extra-sensory perception, which cannot be explained by our knowledge of physics here. However, that phenomenon has not been well established, and we cannot guarantee that it is there. If it could be demonstrated, of course, that would prove that physics is incomplete, and it is therefore extremely interesting to physicists whether it is right or wrong. Many, many experiments exist which show that it doesn't work. The same goes for astrological influences. If it were true that the stars could affect the day that it was good to go to the dentist - in America we have that kind of astrology - then the physics theory would be wrong, because there is no mechanism understandable in principle from these things that would make it go. That is the reason that there is some scepticism among scientists with regard to those ideas.

Now you see of course that with this method we can disprove any definite theory. We have a definite theory, a real guess, from which you can clearly compute consequences which could be compared to experiment and in principle we can get rid of any theory. You can always prove any definite theory wrong. Notice however that we never prove it right.

Suppose you invent a good guess, calculate the consequences, and discover every time that the consequences you have calculated agree with experiment. The theory is then right? No, it is simply not proved wrong.

Another thing I must point out is that you cannot prove a vague theory wrong. If the guess that you make is poorly expressed and rather vague, and the method that you use for figuring out the consequences is a little vague —you are not sure, and you say, “I think everything’s right because it’s all due to so and so, and such and such do this and that more or less, and I can sort of explain how this works...” then you see that this theory is good, because it cannot be proved wrong! Also if the process of computing the consequences is indefinite, then with a little skill any experimental results can be made to look like the expected consequences. You are probably familiar with that in other fields. ‘A’ hates his mother. The reason is, of course, because she did not caress him or love him enough when he was a child. But if you investigate you find out that as a matter of fact she did love him very much, and everything was all right. Well then, it was because she was overindulgent when he was a child! By having a vague theory it is possible to get either result. The cure for this one is the following: if it were possible to state exactly, ahead of time, how much love is not enough, and how much love is over-indulgent, then there would be a perfectly legitimate theory against which you could make tests. It is usually said when this is pointed out--when you are dealing with psychological matters things can’t be defined so precisely. Yes, but then you cannot claim to know anything about it.

Leading Questions - Yes Prime Minister

Bernard Woolley: He's going to say something new and radical in the broadcast.

Sir Humphrey: What, that silly Grand Design? Bernard, that was precisely what you had to avoid! How did this come about, I shall need a very good explanation.

Bernard Woolley: Well, he's very keen on it.

Sir Humphrey: What's that got to do with it? Things don't happen just because Prime Ministers are very keen on them! Neville Chamberlain was very keen on peace.

Bernard Woolley: He thinks ... he thinks it’s a vote winner.

Sir Humphrey: Ah, that’s more serious. Sit down. What makes him think that?

Bernard Woolley: Well the party have had an opinion poll done and it seems all the voters are in favour of bringing back National Service.

Sir Humphrey: Well have another opinion poll done to show that they’re against bringing back National Service.

Bernard Woolley: They can’t be for and against
…
Sir Humphrey: Oh, of course they can Bernard! Have you ever been surveyed?

Bernard Woolley: Yes, well not me actually, my house … Oh I see what you mean

Sir Humphrey: You know what happens: nice young lady comes up to you. Obviously you want to create a good impression, you don’t want to look a fool, do you?

Bernard Woolley: No

Sir Humphrey: So she starts asking you some questions: Mr. Woolley, are you worried about the number of young people without jobs?

Bernard Woolley: Yes

Sir Humphrey Appleby: Are you worried about the rise in crime among teenagers?

Bernard Woolley: Yes.

Sir Humphrey Appleby: Do you think there is lack of discipline in our Comprehensive Schools?

Bernard Woolley: Yes.

Sir Humphrey Appleby: Do you think young people welcome some authority and leadership in their lives?

Bernard Woolley: Yes.

Sir Humphrey Appleby: Do you think they respond to a challenge?

Bernard Woolley: Yes.

Sir Humphrey Appleby: Would you be in favour of reintroducing National Service?

Bernard Woolley: Oh, well I suppose I might.

Sir Humphrey Appleby: Yes or no?

Bernard Woolley: Yes.

Sir Humphrey: Of course you would, Bernard. After all you told you can’t say no to that. So they don’t mention the first five questions and they publish the last one.

Bernard Woolley: Is that really what they do?

Sir Humphrey: Well, not the reputable ones, no, but there aren’t many of those. So alternatively the young lady can get the opposite result.

Bernard Woolley: How?

Sir Humphrey Appleby: Mr. Woolley, are you worried about the danger of war?

Bernard Woolley: Yes.

Sir Humphrey Appleby: Are you worried about the growth of armaments?

Bernard Woolley: Yes.

Sir Humphrey Appleby: Do you think there's a danger in giving young people guns and teaching them how to kill?

Bernard Woolley: Yes.

Sir Humphrey Appleby: Do you think it's wrong to force people to take arms against their will?

Bernard Woolley: Yes.

Sir Humphrey Appleby: Would you oppose the reintroduction of National Service?

Bernard Woolley: Yes.

Sir Humphrey Appleby: There you are, you see, Bernard. The perfect balanced sample.

Twenty tips for interpreting scientific claims by William J. Sutherland, David Spiegelhalter & Mark Burgman

http://www.nature.com/news/policy-twenty-tips-for-interpreting-scientific-claims-1.14183

Differences and chance cause variation. The real world varies unpredictably. Science is mostly about discovering what causes the patterns we see. Why is it hotter this decade than last? Why are there more birds in some areas than others? There are many explanations for such trends, so the main challenge of research is teasing apart the importance of the process of interest (for example, the effect of climate change on bird populations) from the innumerable other sources of variation (from widespread changes, such as agricultural intensification and spread of invasive species, to local-scale processes, such as the chance events that determine births and deaths).

No measurement is exact. Practically all measurements have some error. If the measurement process were repeated, one might record a different result. In some cases, the measurement error might be large compared with real differences. Thus, if you are told that the economy grew by 0.13% last month, there is a moderate chance that it may actually have shrunk. Results should be presented with a precision that is appropriate for the associated error, to avoid implying an unjustified degree of accuracy.

Bias is rife. Experimental design or measuring devices may produce atypical results in a given direction. For example, determining voting behaviour by asking people on the street, at home or through the Internet will sample different proportions of the population, and all may give different results. Because studies that report 'statistically significant' results are more likely to be written up and published, the scientific literature tends to give an exaggerated picture of the magnitude of problems or the effectiveness of solutions. An experiment might be biased by expectations: participants provided with a treatment might assume that they will experience a difference and so might behave differently or report an effect. Researchers collecting the results can be influenced by knowing who received treatment. The ideal experiment is double-blind: neither the participants nor those collecting the data know who received what. This might be straightforward in drug trials, but it is impossible for many social studies. Confirmation bias arises when scientists find evidence for a favoured theory and then become insufficiently critical of their own results, or cease searching for contrary evidence.

Bigger is usually better for sample size. The average taken from a large number of observations will usually be more informative than the average taken from a smaller number of observations. That is, as we accumulate evidence, our knowledge improves. This is especially important when studies are clouded by substantial amounts of natural variation and measurement error. Thus, the effectiveness of a drug treatment will vary naturally between subjects. Its average efficacy can be more reliably and accurately estimated from a trial with tens of thousands of participants than from one with hundreds.

Correlation does not imply causation. It is tempting to assume that one pattern causes another. However, the correlation might be coincidental, or it might be a result of both patterns being caused by a third factor — a 'confounding' or 'lurking' variable. For example, ecologists at one time believed that poisonous algae were killing fish in estuaries; it turned out that the algae grew where fish died. The algae did not cause the deaths².

Regression to the mean can mislead. Extreme patterns in data are likely to be, at least in part, anomalies attributable to chance or error. The next count is likely to be less extreme. For example, if speed cameras are placed where there has been a spate of accidents, any reduction in the accident rate cannot be attributed to the camera; a reduction would probably have happened anyway.

Extrapolating beyond the data is risky. Patterns found within a given range do not necessarily apply outside that range. Thus, it is very difficult to predict the response of ecological systems to climate change, when the rate of change is faster than has been experienced in the evolutionary history of existing species, and when the weather extremes may be entirely new.

Beware the base-rate fallacy. The ability of an imperfect test to identify a condition depends upon the likelihood of that condition occurring (the base rate). For example, a person might have a blood test that is '99% accurate' for a rare disease and test positive, yet they might be unlikely to have the disease. If 10,001 people have the test, of whom just one has the disease, that person will almost certainly have a positive test, but so too will a further 100 people (1%) even though they do not have the disease. This type of calculation is valuable when considering any screening procedure, say for terrorists at airports.

Controls are important. A control group is dealt with in exactly the same way as the experimental group, except that the treatment is not applied. Without a control, it is difficult to determine whether a given treatment really had an effect. The control helps researchers to be reasonably sure that there are no confounding variables affecting the results. Sometimes people in trials report positive outcomes because of the context or the person providing the treatment, or even the colour of a tablet³. This underlies the importance of comparing outcomes with a control, such as a tablet without the active ingredient (a placebo).

Randomization avoids bias. Experiments should, wherever possible, allocate individuals or groups to interventions randomly. Comparing the educational achievement of children whose parents adopt a health programme with that of children of parents who do not is likely to suffer from bias (for example, better-educated families might be more likely to join the programme). A well-designed experiment would randomly select some parents to receive the programme while others do not.

Seek replication, not pseudoreplication. Results consistent across many studies, replicated on independent populations, are more likely to be solid. The results of several such experiments may be combined in a systematic review or a meta-analysis to provide an overarching view of the topic with potentially much greater statistical power than any of the individual studies. Applying an intervention to several individuals in a group, say to a class of children, might be misleading because the children will have many features in common other than the intervention. The researchers might make the mistake of 'pseudoreplication' if they generalize from these children to a wider population that does not share the same commonalities. Pseudoreplication leads to unwarranted faith in the results. Pseudoreplication of studies on the abundance of cod in the Grand Banks in Newfoundland, Canada, for example, contributed to the collapse of what was once the largest cod fishery in the world⁴.

Scientists are human. Scientists have a vested interest in promoting their work, often for status and further research funding, although sometimes for direct financial gain. This can lead to selective reporting of results and occasionally, exaggeration. Peer review is not infallible: journal editors might favour positive findings and newsworthiness. Multiple, independent sources of evidence and replication are much more convincing.

Significance is significant. Expressed as P, statistical significance is a measure of how likely a result is to occur by chance. Thus P = 0.01 means there is a 1-in-100 probability that what looks like an effect of the treatment could have occurred randomly, and in truth there was no effect at all. Typically, scientists report results as significant when the P-value of the test is less than 0.05 (1 in 20).

Separate no effect from non-significance. The lack of a statistically significant result (say a P-value > 0.05) does not mean that there was no underlying effect: it means that no effect was detected. A small study may not have the power to detect a real difference. For example, tests of cotton and potato crops that were genetically modified to produce a toxin to protect them from damaging insects suggested that there were no adverse effects on beneficial insects such as pollinators. Yet none of the experiments had large enough sample sizes to detect impacts on beneficial species had there been any⁵.

Effect size matters. Small responses are less likely to be detected. A study with many replicates might result in a statistically significant result but have a small effect size (and so, perhaps, be unimportant). The importance of an effect size is a biological, physical or social question, and not a statistical one. In the 1990s, the editor of the US journal Epidemiology asked authors to stop using statistical significance in submitted manuscripts because authors were routinely misinterpreting the meaning of significance tests, resulting in ineffective or misguided recommendations for public-health policy⁶.

Study relevance limits generalizations. The relevance of a study depends on how much the conditions under which it is done resemble the conditions of the issue under consideration. For example, there are limits to the generalizations that one can make from animal or laboratory experiments to humans.

Feelings influence risk perception. Broadly, risk can be thought of as the likelihood of an event occurring in some time frame, multiplied by the consequences should the event occur. People's risk perception is influenced disproportionately by many things, including the rarity of the event, how much control they believe they have, the adverseness of the outcomes, and whether the risk is voluntarily or not. For example, people in the United States underestimate the risks associated with having a handgun at home by 100-fold, and overestimate the risks of living close to a nuclear reactor by 10-fold⁷.

Dependencies change the risks. It is possible to calculate the consequences of individual events, such as an extreme tide, heavy rainfall and key workers being absent. However, if the events are interrelated, (for example a storm causes a high tide, or heavy rain prevents workers from accessing the site) then the probability of their co-occurrence is much higher than might be expected⁸. The assurance by credit-rating agencies that groups of subprime mortgages had an exceedingly low risk of defaulting together was a major element in the 2008 collapse of the credit markets.

Data can be dredged or cherry picked. Evidence can be arranged to support one point of view. To interpret an apparent association between consumption of yoghurt during pregnancy and subsequent asthma in offspring⁹, one would need to know whether the authors set out to test this sole hypothesis, or happened across this finding in a huge data set. By contrast, the evidence for the Higgs boson specifically accounted for how hard researchers had to look for it — the 'look-elsewhere effect'. The question to ask is: 'What am I not being told?'

Extreme measurements may mislead. Any collation of measures (the effectiveness of a given school, say) will show variability owing to differences in innate ability (teacher competence), plus sampling (children might by chance be an atypical sample with complications), plus bias (the school might be in an area where people are unusually unhealthy), plus measurement error (outcomes might be measured in different ways for different schools). However, the resulting variation is typically interpreted only as differences in innate ability, ignoring the other sources. This becomes problematic with statements describing an extreme outcome ('the pass rate doubled') or comparing the magnitude of the extreme with the mean ('the pass rate in school x is three times the national average') or the range ('there is an x-fold difference between the highest- and lowest-performing schools'). League tables, in particular, are rarely reliable summaries of performance.

Nature 503, 335–337 (21 November 2013)

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