Debunking Denialism

Fighting pseudoscience and quackery with reason and evidence.

Category Archives: Science Explained

How to Catch a Serial Killer

How are serial killers caught?

Crime shows and police procedure dramas (like Criminal Minds and Law and Order) that flood our television experience give the appearance that serial killers are caught by the use of criminal profiling and sophisticated forensic tools such as fingerprint analysis, DNA technology, digital tracking, blood spatter analysis, ballistic comparisons and many more. But how much of it is real? Are criminal profiling and forensic science really responsible for capturing most serial killers?

White, Lester, Gentile and Rosenbleeth (2011) investigates this question by studying 200 serial killers. They found that although forensic evidence was often key in getting a conviction, no serial killer was captured by the use of forensic evidence or criminal profiling. Instead, the reason serial killers were caught was traditional police work and communication with the public.

What is a serial killer?

For the purpose of this paper, a serial killer is defined as:

a person who has killed at least three people at different locations with a ‘cooling off’ period between the killings”

Special accommodations were made for a minority of repeated killers who killed at home (Gacy and Dahmer) or at a hospital (angel of death). This is different from a mass killer or mass shooter who, depending on definition, kills 3-4 people in the same general location and time.

What was the sample size and how was the sample selected?

A total of 200 serial killers were included in this study. Facts about the serial killers in the sample was taken from “newspaper reports, true crime books, and encyclopedias” and then “referenced with other sources”. The identity of these “other sources” are left unspecified.

What role did criminal profiling / forensic science play in catching serial killers?

None of the serial killers were identified or captured by criminal profiling or forensic science alone. Not a single one. The authors write:

Interestingly, not one serial killer in the present study, albeit limited to 200 subjects, was captured by forensic evidence alone, without the help of the public or the investigative acumen of the police by interviewing the public.

It should be noted, however, that forensic science such as DNA evidence, often played a crucial role in attaining a conviction against the serial killers in this sample. Thus, in contrast to police procedural dramas such as Criminal Minds, criminal profiling and forensic evidence plays a minor role in identifying and finding serial killers.

How are serial killers caught?

So if criminal profiling and/or forensic evidence does not play a leading role in identifying and capturing serial killers, how are they captured?

Read more of this post

How to Breach Genetic Privacy

Breaching genetic privay

Massive parallel sequencing technology has opened up endless possibilities in areas such as diagnosing clinical conditions, finding new drug targets, predicting disease risk and fighting crime. A room with twenty modern sequencing machines can sequence around a thousand human genomes per day. Most practical applications require knowledge of only a tiny section of the genome, which means that the rate at which genetic information can be acquired is truly astonishing. With it comes serious ethical considerations. What happens if your genetic information leaks and can be accessed by employers, insurance companies or adversaries with an axe to grind?

Erlich and Narayanan (2014) describe some of the techniques that can be used to breach the genetic privacy of individuals (with real-world examples of exploits) and discuss some of the methods that can be used to safeguard it from intruders.

How adversaries can breach genetic privacy

There are three larger categories of attacks: based on identity tracing, attribute disclosure using DNA, and completion attacks. Identity tracing is based on meta-data from scientific research, such as genotypic sex, date of birth, zip code and surname. Attribute disclosure attacks are based on accessing the genetic information of a person and then matching it against an anonymous sample linked to sensitive information. Finally, completion attacks allows the inference of target genotypic information based on other areas of the target genome or the genomes of relatives.

Identity tracing attacks

Identity tracing attacks starts with genomic information from an unknown individual. However, this is usually associated with metadata in the form of quasi-identifiers, such as genotypic sex, age, date of birth, zip code, surname and so on. Armed with this information, the adversary can drastically narrow down the range of possible targets to a small group, and then pin-point the individual with the help of information found social media websites such as Facebook. This is done with a wide range of techniques, such as surname inference, DNA phenotypic, demographic identifiers, pedigree structure and side-channel leaks.

Read more of this post

How Modern Genomics Crushed Bigfoot Pseudoscience

Bigfoot? Or just a guy in a suit?

Thousands of people around the world believe in the existence of a large primate that roams the mountain forests. It is known by many names, such as Bigfoot, Yeti and Sasquatch. Many of these enthusiasts even claim to have genuine biological samples from these creatures. Skeptics have so far remain unconvinced. No authentic photographs or video material has been produced (the one on the right is a man in a suit) and no bodies have been found. Meanwhile, cryptozoologists complain that scientist are not taking them seriously.

To remedy this problem, Sykes et. al. (2014) requested samples from all over the world, subject them to rigorous decontamination protocols, amplified the DNA and then sequence them in order to find out their identity. Guess what they found?

Read more of this post

Risk Factors: Misunderstandings and Abuses

Risk factors

Although risk factors occupy a central place in medical and epidemiological research, it is also one of the most misunderstood concepts in all of medicine.

The World Health Organization (2009) defines a risk factor as: “A risk factor is any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury. Some examples of the more important risk factors are underweight, unsafe sex, high blood pressure, tobacco and alcohol consumption, and unsafe water, sanitation and hygiene.” The CDC (2007) offers a similar definition: “an aspect of personal behavior or lifestyle, an environmental exposure, or a hereditary characteristic that is associated with an increase in the occurrence of a particular disease, injury, or other health condition.” However, the CDC also uses the term risk factor when it comes to sexual violence. For instance, they consider alcohol and drug use, antisocial tendencies, hostility towards women, and community-level tolerance to sexual violence.

Based on these sources, we can develop a simplified definition of a risk factor: if A is a risk factor for B, then the presence of A increases (but not necessarily in a causal sense) the probability of B occurring.

A is a risk factor for B does not necessarily mean that A causes B. It might be the case that A causes B only indirectly via some third factor, that B causes A, or that some third factor causes both A and B. In other words, correlation does not on its own imply causation. However, it is possible to disentangle these possibilities by measuring B at the start of the study. If physical punishment of children is a risk factor for aggressiveness, we can find out what comes first by measuring baseline child aggressiveness.

A is a risk factor for B does not mean that A will cause B in every instance of A. Smoking causes lung cancer, but some smokers can smoke all their lives without developing lung cancer. This does not mean that smoking is not a cause of lung cancer. It just means that there are other factors that also play a role. It is common for pseudoscientific cranks to bring up exceptions of this kind to argue against a correlational or causal association in an effort to spread uncertainty and doubt. Read more of this post

Half of Americans Believe in Medical Conspiracy Theories

Medical conspiracy theories

An interesting study was recently published in JAMA Internal Medicine by Oliver and Wood (2014). They report the results of a YouGov survey that looked at the acceptance of medical conspiracy theories in the United States and what, if any, effect the belief in medical conspiracy theories had on health-related behavior, such as taking herbal supplements, getting a flu shot and preference for organic foods. The results were chilling as almost half of the U. S. population believed in at least one medical conspiracy. Those who held three or more were less likely to go to the doctor or dentist and fewer got vaccinated against seasonal influenza. They were also more likely to take herbal supplements.

The selection of medical conspiracy theories

Oliver and Wood selected six different medical conspiracy theories to include in their research. Although the researchers did not justify their selection, it seems representative and wide as it spanned from FDA and alternative medicine to discredited beliefs about the origin of HIV Read more of this post

The Pitfalls of fMRI-Based Lie Detection

fMRI-based lie detection

A while ago, an interesting paper on the promise and pitfalls of fMRI-based lie detection was published by Farah, Hutchinson, Phelps and Wagner (2014) in Nature Reviews Neuroscience. It is part of an ongoing article series by the journal examining the interplay between neuroscience and law. This installment discussed the reliability of observed associations between certain brain areas and deception, current limitations of fMRI-based lie detectors, how U. S. courts have treated appeal to fMRI data put forward as evidence as well as ethical and legal issues with the procedure. This post will also discuss ways of beating an fMRI-based lie detector.

Another article in that series that deals with common misconceptions about memory, memory distortions and the consequence of ignorance was covered here.

How does fMRI work?

An fMRI indirectly measure brain activity by measuring blood-oxygen-level dependent (BOLD) activity. This typically involve a lot of controls to make sure that researchers capture the neural correlates of what they want to study instead of irrelevant confounders. Typically, researchers compare BOLD activity during deception and truth-telling in an attempt to find the BOLD-signature of deception, which would give clues about the neural correlates of deception (i.e. patterns of brain activation associated with deception).

The theoretical rationale for fMRI-based deception is that there is probably a relationship between deception and cognition because deception is more demanding on memory and various executive functions than truth-telling.

What are the neural correlates of deception?

The paper performed a meta-analysis with the activation likelihood estimation (ALE) method. This is a way to measure overlap in neuroimaging data based on so-called “peak-voxel coordinate information” and thereby find out how reliable the association between deception and certain brain regions is. After applying their specific inclusion criteria, they identified 23 relevant studies. Their meta-analysis identified several areas as being associated with deception e. g. parts of the prefrontal cortex, the anterior insula and inferior parietal lobule. However, the between-study variation was enormous and no region was always identified.


Despite the apparent high identification rate of deception, fMRI-based lie detection has a long list of very important limitations that effectively undermine any confidence in this technique for legal purposes Read more of this post

Butchering Scientific Studies


Sometimes, people who promote pseudoscience online try to reference the scientific literature. In one sense, this is progress. They are going from just making arbitrary assertions to trying to justify them. In another sense, it is a turn for the worse. That is because the papers they reference are either of incredibly low scientific quality or rarely support what is being claimed. However, the behavior gives the illusion of evidential support for some readers. A lot of the time, they damage their own position by spamming long lists of links to videos and blog articles, but some promoters of pseudoscience are more sophisticated.

Previously, I wrote a short introduction on how to counter cranks that reference the scientific literature. Consider this to be the intermediate to advanced version. It will attempt to provide scientific skeptics with additional tools to counter pseudoscience online. The focus will be on research articles, specifically clinical trials. However, the general arguments can often be extended to other forms of research articles. Some of the tools are evidential or methodological in nature and directly related to the meat of the article such as whether or not there was a control group or control for confounders, the appropriateness of the statistical analysis and whether the conclusion accurately reflected the results. Others are more sociological in nature, looking at the journal itself, the presence or absence of peer-review, impact factor, who the authors are etc. These do not necessary count against the research in the article directly and should not be used alone, but provide useful external arguments if combined with criticisms of the study itself. There is of course some overlap between and within these broad categories.

First, a word of warning. Knowledge can be used for good or evil, and this is no exception. It is very dangerous to find oneself in a situation when the studies that run counter to one’s position are subjected to merciless criticisms while the research that support it is being accepted with little or no critical thought. This is known as pseudoskepticism and something to avoid at all cost. It can even undermine the rationality of some of the giants in science seemingly without difficulty. Read more of this post

How HIV/AIDS Denialists Abuse Bayes’ Theorem

Image by Matt Buck, under Attribution-ShareAlike 2.0 Generic.

bayestheorem in neon

Note: Snout (Reckless Endangerment) has made some good arguments in the comment to this post. The gist is that HIV/AIDS denialists overestimate the false positive rate by assuming that the initial test is all there is, when in fact, it is just the beginning of the diagnostic process. Snout also points out that it is probably wrong to say that most people who get tested have been involved in some high-risk behavior, as a lot of screening goes on among e. g. blood donors etc. I have made some changes (indicated by del or ins tags) in this post because I find myself convinced by the arguments Snout made.

There have already been several intuitive introductions to Bayes’ theorem posted online, so there is little point in writing another one. Instead, let us apply elementary medical statistics and Bayes’ theorem to HIV tests and explode some of the flawed myths that HIV/AIDS denialists spread in this area.

The article will be separated into three parts: (1) introductory medical statistics (e. g. specificity, sensitivity, Bayes’ theorem etc.), (2) applying Bayes’ theorem to HIV tests to find the posterior probability of HIV infection given a positive test result in certain scenarios and (3) debunking HIV/AIDS denialist myths about HIV tests by exposing their faulty assumptions about medical statistics. For those that already grasp the basics of medical statistics, jump to the second section.

(1) Introductory medical statistics

A medical test usually return a positive or a negative result (or sometimes inconclusive). Among the positive results, there are true positives and false positives. Among the negative results, there are true negatives and false negatives.

True positive: positive test result and have the disease.
False positive: positive test result and do not have the disease

True negative: negative test result and do not have the disease.
False negative: negative test result and have the disease.

For the purpose of this discussion, + will indicate a positive test, - will indicate a negative test, HIV will indicate having HIV and \neg HIV will indicate not having HIV.

P(A) is the probability of an event A, say, the probability that a fair dice will land on three. Conditional probabilities, such as P(A \mid B) , represents the probability of event A, given that event B has occurred. If A and B are statistically independent events, then P(A \mid B) = P(A) , if P(B) \neq 0 (because the definition of P(A \mid B) has P(B) in the denominator).

Let us define some conditional probabilities that are relevant for HIV tests and Bayes theorem: Read more of this post

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