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

The Widespread Abuse of Heritability

stack of books

Together with evolution, heritability is perhaps one of the most misunderstood and abused concepts in biology.

Some white supremacists appeal to moderate to high estimates of heritability for phenotypic traits to justify genetic determinism, that genes explain between-group differences, the discrimination of ethnic groups or other malignant and pseudoscientific beliefs that are incompatible with science.

Some egalitarian dislikes scientific results regarding moderate to high heritability estimates because they believe that it indicate that the environment is unimportant in explaining the phenotype of individuals and latch onto single studies showing low heritability as if that meant that genes are less important.

As we shall see, both of these groups believe things that are flawed from a scientific standpoint. But before we discuss why and how this is, it might be beneficial to know something about what heritability actually is. Our definition of heritability will be unpacked and improved in several stages to facilitate understanding. Read more of this post

Some Falsehoods about the Y chromosome and Male Brains

y chromosome

Note: Greg Laden has made a comment on this post saying that I misrepresented his position. I am open to the possibility and have therefore asked some follow-up questions, but at the time of writing this note (2012-07-26 22:23 GMT +1 DST), Laden has not clarified the situation for me. Keep this in mind while reading this post. Will update this again when he does.

Note: I just noticed (2012-07-28 22:08 GTM +1 DST) that Heina Dadabhoy did not mean what she actually said, but said it as a joke in response to a tweet by Zvan. There is an alternative explanation, namely as a post hoc rationalization when Heina discovered that she had been called on it, but it seems less likely. In essence, this means that we can probably consider both the claim made by Greg and Heina to be jokes or awkwardly expressed science. The only think left now is for Greg to finish writing up his follow-up and/or setting me straight by explaining more in detail in what way I misrepresented him.

Note: As a clarification (2012-07-28 23:06 GMT +1 DST) for Kelseigh Nieforth (‏@Nezchan), I reject this alternative explanation. It is possible, but relatively implausible. I did not intend to sound “mean-spirited & insulting”, quite the opposite. My intent was to rebuke what I felt was going to be the standard misogynist reply (i.e. claiming that Heina only said it was a joke when she noticed it had gotten a lot of attention and reflected badly upon her).

Note: Greg Laden has clarified his position over at his Scienceblogs blog. The general idea is that testosterone alters the male brain during different stages of development and “damaged” referred to the fact that androgens and other biosocial factors influence certain men to be more statistically likely to exhibit socially noxious and harmful behaviors that are incompatible with progressive, egalitarian and peaceful world. I have no general problems with this position (note added 2012-08-03 20:16 GTM +1 DST).

Note: This blog post has been linked by a men’s rights activist blog. All forms of discrimination is morally wrong, but most men’s rights activism I have come across seems to be equal parts pseudoscience and blanket anti-feminism. I therefore, in general, reject men’s rights activism. This post should not, and cannot, be interpreted as giving men’s rights activism any support, whatsoever (clarification added 2012-08-04 14:14 GMT +1 DST).

The background to this story is that Heina Dadabhoy and Greg Laden, at a panel discussion on gender differences at SkepchickCon/CONvergence, claimed that the Y chromosome was “broken” and that the male brain is a female brain damaged by testosterone. Amidst substantial criticism of these claims, the FtB blogger Stephanie Zvan decided to take upon herself to defend these flawed notions. As we shall see, her attempt is filled with incorrect characterizations and selective use of the scientific literature,

But first, let us make sure we have understood the claims being put forward in the video, so that we do not incorrectly characterize them as something they are not. A video of the panel discussion can be found here. I will post enough of the discussion for context, but readers are encouraged to check if I have gotten everything right. Laden was especially hard to take a transcript of, because he talks very fast and often changes mid-sentence, but hopefully I got the gist. It starts with a question from the audience at 35:41 about the gender differences in autism diagnosis and how males are supposedly more often autistic than females:

Heina Dadabhoy: That is an underdiagnosis issue, actually. They have been doing more and more research on women and autism. A lot of us women who fall on the spectrum only find out when we are adults, because a lot of the behaviors that manifest…the ways that girl tend to manifest it is slightly different and you know a girl who gets obsessed with something they are like “oh, well she is a girl and she has her little obsessions, how cute and when it is a boy it is like “oh, why isn’t he out beating up his peers?” so that is a big issue with autism.

Member in the audience: …inaudible… [probably something to do with differential disease susceptibility between genders e. g. red-green color blindness or hemophilia – E. K.]

Heina Dadabhoy: That is the Y chromosome. It’s broken [Dadabhoy smiles and laughs – E. K.]

Greg Laden: There is… there is … One thing that psychology does…There is some reasonable evidence that certain….There are gender differences.. [inaudible]. But there are gender differences. One of the most important gender differences.. in other words males versus females do not overlap that much at all… in certain areas and one of…one place they do not overlap at all, and you can’t change this… with culture… you can change spatial orientation by giving everyone Tetris when they are born and will be the same. What we can’t change is that, for example, is the number of kids that cannot read until much later…the age at which you start to read and how you have dyslexia and so on that are boys is an order of magnitude higher in girls and you can do everything you want to fix that and you can only fix them a little bit. Most of those differences disappear and are not necessarily that significant, but is real. You know, the male brain is a female brain damaged by testosterone in various stages in it’s life. I think probably there are some very interesting adult difference…you cannot look at at a person and say that, but population differences between males and females that has to do with brain development because hormonal differences and…most of them are probably kind of trivial but there probably are some…yeah autism…I don’t think that is an example of one, but there probably are some things but if we where that different, it would be a hard time communicating…[inaudible].

So, right of the bat we can see that Zvan has incorrectly characterized both what Dadabhoy and Laden had stated. Dadabhoy stated that the Y chromosome was broken, not, as Zvan wants to have it that the Y chromosome is a broken X chromosome. Laden stated that male brain is a female brain damaged by testosterone in various stages in it’s life and did not use the term development. As we shall see, it is these false characterizations that Zvan’s bases her arguments on, but the bigger problem is that Zvan has no scientific foundation for her argument, leading the entire tortuous justification of the notion that men are genetically and neurologically “broken” to collapses onto itself.

The Y chromosome is not broken, but contains 86 unique and functioning genes

In her attempt to justify the absurd notion that men are genetically broken, Zvan appeals to the fact that the Y chromosome cannot recombine with the X chromosome to the same degree that the X chromosome can with another X chromosome. While this is true, this does not justify the original claim that the Y chromosome is a broken X chromosome, or the stronger claim that the Y chromosome is broken. In fact, the Y chromosome contains 86 fully functioning genes and this does not even count the genes that exists on both the X and Y chromosome. For the vast majority of individuals, the Y chromosome is fully functional and does not produce genetic defects or pathology. So nothing is actually “broken”.

X-linked recessive disorders signify a problem with the X chromosome, not the Y one

Zvan points out that males are more at risk for certain heritable disease because the related gene only occurs once, while in females it occurs twice (since they have two X chromosomes). This is also true, but the causative factor is the disabling mutation in the X chromosome that causes the disease, not something to do with the Y chromosome. So in other words, what is “broken” in these cases, is the X chromosome, not the Y.

Lack of large-scale recombination is sometimes a good thing

The loss of an ability for large-scale recombination is not something uniformly bad. In fact, if large-scale recombination between the Y chromosome and X chromosome was possible, it could result in males without the necessary sex-determining or sex-influencing regions in their Y chromosomes and females with harmful genes only found on the Y chromosomes, so the lack of large-scale recombination between X and Y is clearly adaptive. A loss does not need to be evolutionary or physiologically detrimental. Read more of this post

Genetic Risk Factors and Parental Responsibility

The interaction of nature and circumstance is very close, and it is impossible to separate them with precision. Nurture acts before birth, during every stage of embryonic and pre-embryonic existence, causing the potential faculties at the time of birth to be in some degree the effect of nurture. We need not, however, be hypercritical about distinctions; we know that the bulk of the respective provinces of nature and nurture are totally different, although the frontier between them may be uncertain, and we are perfectly justified in attempting to appraise their relative importance.

– Sir Francis Galton, Inquiries into human faculty and its development (1883).

The nature versus nurture (or biology versus the environment) controversy has raged on for thousands of years. Modern science, however, has rejected this dichotomy as trivially false. It is not nature versus nurture, but nature through nurture. Both play essential roles in shaping organisms such as ourselves and they often interact with each other. However, as Galton remarked above, one could still discuss the relative merits of partial biological and environmental explanations. When people reduce the complex interaction of biology, psychology, biological and social environment to “mostly biology” or “mostly environment”, they are perpetually restraining humanity into the black-and-white cage that is nature versus nurture, despite paying lip service to modern science. Worse is that “mostly biology” is incorrectly interpreted as some form of genetic determinism, whereas “mostly environment” is erroneously conceived as the notion of the blank slate and the hail of vitriolic straw man arguments begin. The fact that some Internet commentators, journalists and other interested parties do not have sufficient scientific understanding, especially with regards to biology and psychology, makes it even more troubling. This, in turn, leads to a lot of misunderstandings about the science.

Clearly not the best setup for an intellectually productive discussion. Read more of this post

The Intellectual Bankruptcy of Eugenics

For the purpose of this article, eugenics is defined as “the belief that certain individuals should be killed, be forced to undergo sterilization or other be exposed to other coercive measures to prevented them from reproducing in order to protect the population from harm and to ensure the genetic quality of future generations”. I will occasionally attribute other beliefs to eugenics, such as beliefs in “racial purity” or that evolutionary beneficial implies moral, so let’s consider this a working definition for now. Yes, I am aware that there are people who support other forms of eugenics based on voluntarism etc. but those groups are not the target here.

As we shall see, there are many problems with eugenics. It is based on a multitude of scientific falsehoods, has huge practical problems, it is arguably not cost-effective and wildly unethical. Some of these points are somewhat overlapping, but they emphasize specific problems.

1. Eugenics is based on artificial selection, but this is in practice mainly useful for selecting genes with additive effects. However, most genes have interacting effects, making eugenics less efficient, although not impossible.

2. Eugenics is based on a naive view of development. There is hardly never a direct 1:1 relationship between one gene and one phenotypic trait. In general, most traits are polygenic (influenced by many genes) and most genes are pleiotropic (affect many different traits). It is more accurate to think of the situation as a huge, complex network of genes and gene products influencing each other. The heritability of personality traits and certain complex hereditary diseases tend to be moderate (calculated from twin and adoption studies). Using Genome-wide association studies to analyze hundreds of thousands of single nucleotide polymorphisms (SNPs), scientists have found that candidate SNPs can only account for a fraction of his heritability (“missing heritability problem”). This may be accounted for by rare gene variants that are unique for different populations, variation in copy number or genetic interactions.

3. Eugenics is based on a naive view of the power of genes. Genes tend to be risk factors for certain conditions, where environment can act as the trigger. A classical example is the condition know as phenylketonuria (PKU). The genetic risk factor is a mutated version of a gene coding for the enzyme known as phenylalanine hydroxylase that catalyze the hydroxylation of the amino acid phenylalanine to tyrosine. When this is non-functional, phenylalanine accumulates and is converted to phenylketones. This in turn causes mental retardation, brain damage and seizures. An incredibly successful treatment is a diet free of phenylalanine and monitoring of the blood levels of this amino acid. In this case, environmental interventions are more beneficial, cheaper and less unethical than eugenics.

4. If you imagine the general problem outlined in point 3, but instead think of it being hundreds of different genetic and environmental risk factors, then you have an approximate view of most complex human diseases.

5. Even for so called single gene disorders, an individual with one copy of the defect allele and one copy of the healthy allele may have a selective advantage. The classic example is that a person heterozygous for the allele that in the homozygous condition causes sickle-cell anemia has a higher resistance to malaria. The allele, although detrimental in the homozygous condition, is retained in the population by balancing selection. Eliminating gene variants that cause disease in the homozygous condition may lead to less prevalence of individuals with heterozygous advantage. Read more of this post

Belief and Knowledge

Belief and knowledge - a plea about language

Helen Quinn is a particle physicists at the Stanford Linear Accelerator Center and a former president of the American Physical Society and also has been involved in science education and the public understanding of science. Quinn has written an extremely important article that was published a few years ago in the journal Physics Today called Belief and knowledge—a plea about language, dealing with how well-defined scientific concepts are sometimes misunderstood and even abused by the public, which is often incredibly frustrating.

Quinn starts with a personal anecdote: as her husband where describing the topic of his thesis to a layperson, which was using a coincidence set up for to see that two particles detected simultaneously where most likely coming from the same event.

I remember the puzzlement of a friend as my husband described his thesis research—a coincidence experiment. His listener stopped listening; she was thinking about why anyone would try to measure coincidences. I pointed out that the word “coincident” simply means “occurring at the same time.” The experiment used its precise timing to ensure that two particles detected at the same time had a very high probability of coming from the same source event. Thus the term coincidence was used in a sense opposite to the everyday meaning, where a coincidence is two uncorrelated events that come together. Words shift their meaning; each community develops its own usage. That change in meaning leads to miscommunication.

Quinn points out that this problem does not just arise with the term coincident, but with other terms, such as theory and energy and explains how this can lead to misunderstanding of science. Read more of this post

Summary of Victor Stenger’s Case against the Fine-tuning Argument

Victor Stenger is a physicist, philosopher and prolific author, and has recently published the book The Fallacy of Fine-tuning: Why the Universe is Not Designed for Us. It contains perhaps the best currently existing response to the creationist argument from fine-tuning from the perspective of physics. Now, other philosophical and mathematical responses exists, but this is a comprehensive overview of the scientific case against the fine-tuning argument. I will summarize some of the more interesting parts of Stenger’s case below by paraphrasing certain parts of the last part of the final chapter in the book (pp. 293-294) as well as mentioning other problems mentioned in other parts of the book.

1. Many proponents of the fine-tuning argument quote Stephen Hawking out of context to try and show that Hawking thinks that the expansion rate of the universe is fine-tuned. In reality, Hawking just lists this problem as a problem for the big bang theory before cosmological inflation is taken into account. When it is, the fine-tuning problem of the expansion rate goes away.

2. Many proponents of the fine-tuning argument appeal to the singularity theorem proved by Hawking and Penrose in order to try and established that the universe began in a singularity. However, a singularity would be very massive and have infinitesimal volume. This is forbidden in quantum mechanics due to Heisenberg’s uncertainty principle, which states (in one of its versions) that the uncertainty in momentum times the uncertainty in position cannot be less than a specific non-zero number. Thus, the theorem proved by Hawking and Penrose is not applicable anymore.

3. Claims about fine-tuning are made against the backdrop of our particular form of life, yet other forms of life may be possible. Read more of this post

The Top Five Most Annoying Statistical Fallacies


Mathematical statistics and probability is hard. It often involves what, at a first glance, involves complicated calculations and the sheer volume of data coming out of some studies can often be hard to interpret, even if you know all of the mathematics behind it. Although it is important to understand the math, it is equally important (or perhaps even more important) to understand what the results mean and don’t mean. It is easy to get dazzled by fancy mathematics or over-interpret results to mean something they really do not. Therefore, a basic understanding of statistical fallacies should be a part of every scientific skeptics toolbox or baloney detection kit.

Here is a list of the most common statistical fallacies, what they are and how to combat them.

1. Confusing correlation with causation

A correlation is when two variables vary together, whereas causation occurs when one factor causes the other. It may be tempting to think that the former implies the latter, but that is hardly ever the case. For instance, ice cream sales may increase in the summer and decrease in the winter. The same may be true for drowning accidents. Does this mean we can draw the conclusion that drowning accidents causing ice cream sales? Does this mean that people have become so selfish and morally vile that they prefer to buy ice cream and watching people drown than trying to save them!? Fortunately, not really. Just because two variables vary together does not mean that one caused the other. It might be that the other caused the first, that they both cause each other or that a third factor causes both. In the case of ice cream sales and drowning accidents, a third factor that probably explain the correlation is season. In the summer, more people eat ice cream and go bathing, but fewer to these things in the winter. Confusing correlation with causation is widespread in many areas of pseudoscience, such as the anti-vaccination movement; one of their claims is that as the number of vaccines given have increased, so has the rates of cancer. This shows that the two factors correlate, not that vaccines caused cancer (in fact, the vaccine against HPV and Hepatitis B can prevent cancers) is a correlation, not a causation. A more likely factor is better healthcare as a third factor; better healthcare has meant more vaccines, but also increased lifespan, which is associated with an increase in the risk of cancer.

2. Post hoc

Post hoc and denotes the fallacy of thinking that A causes B just because B follows A in time. This fallacy, like the fallacy of confusing correlation with causation, is understandable from an evolutionary perspective. Those that where too skeptical of attributing an upset stomach to poisonous berries where less likely to reproduce. However, this kind of instinct-based reasoning can no longer be thought of as justified in our modern society Read more of this post

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