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RS28 - Live! How To Tell Science From Bunk

Release date: February 13, 2011

Massimo and Julia sit down in front of a live audience at the Jefferson Market Library in New York City for a conversation about science, non-science, and pseudo-science. Based on Massimo's book: "Nonsense on Stilts: How to Tell Science from Bunk" the topics they cover include whether the qualitative sciences are less reliable than quantitative ones, the re-running of the tape of life, and who is smarter: physicists, biologists, or psychologists? Also, why are evolutionary psychologist so fixated on sex?

The live Q&A follows in episode 29

Reader Comments (8)

I find it a little ironic that on the one hand Massimo acknowledges correctly that psychology doesn't have an overarching theory, but on the other hand rejects most of the research in evolutionary psychology. Evolutionary psychologists are claiming for years that the evolutionary principles should be a central part of any displine within psychology (social, clinical, developmental, etc).

Additionally, in response to the claim that for certain traits we only have a sample of one (humans) so we can't know the function of the trait, I must say that I find it a bit odd. If we weren't talking about humans, in any other animal if we saw something that was unique to that animal, we will never claim that we can't know why the trait evolved. For any particulal trait we should look for a function in evolutionary terms. Can this trait help or helped us survive or reproduce? what evidence we can find to support this claim? Moreover, if we find a trait that only humans have, we can pretty much conclude that it evolved recently. We might not have all the evidence that will lead us to the evolution of the trait but we can many times find some evidence to support certain hypothesis. There are some scenarios more plausible than others. In the case of religion for example, there is fossil evidence that humans, as well as neanderthals were involve in ceremonial burial of the dead (we find a lot of symbolic artifacts buried with them), including god like sculptures). These may give us some clues on how religion might evolved or at least playd a role in our ancestors life.

Another clear example is language. Clearly, our spoken language was part of natural selection. Does it means that just because we are the only one speaking there is no way to study its' origin?

February 15, 2011 | Unregistered CommenterGil

I thought this podcast was something of a breakthrough - presenting science information to people as if the listener is an intelligent being. Way too often we are given explanations like - "believe in global climate change because more than 90% of climate scientists do." - Well more than 90% of astrologers believe in horoscopes, but I don't.

Here being up front about concepts like coefficient of correlation and factors of causality actually provides some useful framework. To be a rational person we need to have rational arguments, and understand where scientific information comes from, and it's limits.

February 20, 2011 | Unregistered CommenterJerry

Long time listener, first time commenter. Just finished listening to the latest pod cast.

At one point, the topic of Frequentest vs Bayesian statistics came up. Massimo said that as the sample size became large, Frequentist (classical) statistics becomes biased. This is not in general correct. Most often classical statistics starts with an unbiased estimator and the variance of the estimator goes down as the amount of data increases. If large amounts of data are introducing a bias then your doing something wrong.

Bayesian statistics start with a bias estimator and as more data is collected the bias and variance are reduced. We accept bias for a reduction in variance.

With small amounts of data Bayesian and classical estimators can produce different answers. If you picked a good prior probability (good starting guess) then the Bayesian estimator will out perform the classical estimator. If your prior is bad then the classical estimator may outperform the Bayesian.

With large amounts of data the Bayesian and the classical estimators should produce approximately the same answer. So, it is when the data sets are large the choice between Bayesian and classical is less important.


February 21, 2011 | Unregistered Commentercarbone1853

1. This is one of the most interesting and intellectually stimulating discussions that I've heard in awhile. Thanks for this fine example of public science at its best.
2. The "Father of Psychology" is generally recognized to be Wilhelm Wundt (not James or Freud) who established the first experimental psychology lab @Liepzig in 1879. His student, Edward Titchener (British), established the first experimental lab in the US @Cornell.
3. I think (though I am not 100% certain) that one of the reasons Baysian statistics are preferred for large sample sizes is that, as sample size increases, so does variability.
4. An exception to MP's statements re: psychology would be psychologists such as Jeffrey Schall @Vanderbilt.

February 26, 2011 | Unregistered CommenterClara B. Jones

Here's the difference between frequentist and Bayesian analysis.
The usefulness of a hypothesis test depends on its probability of detection (Pd) and its false alarm rate (Pfa). A perfect test has Pd=1, Pfa=0. A useless test has Pd=Pfa.
Frequentist analysis just limits the false alarm rate to Pfa<0.05. It works as long as Pd is high and the alternative hypothesis is not too improbable, but notice that the test could still be useless if Pfa=Pd.
Bayesian analysis looks at the ratio of Pd/Pfa, called the Bayes factor. Notice that it's infinite for a perfect test and 1 for a useless test.
The Bayes factor says how much the test shifts the odds in favor of the alternative hypothesis. If the odds before the test were 100:1 against the alternative, you need Pd/Pfa >100 to accept the alternative. Notice that if Pfa=0.05, the biggest Bayes factor you can get is 1/0.05=20.

March 1, 2011 | Unregistered CommenterMax


December 9, 2012 | Unregistered CommenterMichael Zacharie
Good stuff. Learn something new everyday. ; )
October 17, 2016 | Unregistered CommenterErnesto
Bayesian probability seems superior to ordinary (frequentest) probability for analyzing experimental results.
February 3, 2018 | Unregistered CommenterJameson

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