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Death Metal Music Inspires Joy Not Violence, Study Finds (bbc.com)
An anonymous reader quotes a report from the BBC: I've had one desire since I was born; to see my body ripped and torn. The lyrics of death metal band Bloodbath's cannibalism-themed track, Eaten, do not leave much to the imagination. But neither this song -- nor the gruesome lyrics of others of the genre -- inspire violence. That is the conclusion of Macquarie University's music lab, which used the track in a psychological test. It revealed that death metal fans are not "desensitized" to violent imagery. The findings are published in the Royal Society journal Open Science. How do scientists test people's sensitivity to violence? With a classic psychological experiment that probes people's subconscious responses; and by recruiting death metal fans to take part. The test involved asking 32 fans and 48 non-fans listen to death metal or to pop whilst looking at some pretty unpleasant images.
Lead researcher Yanan Sun explained that the aim of the experiment was to measure how much participants' brains noticed violent scenes, and to compare how their sensitivity was affected by the musical accompaniment. To test the impact of different types of music, they also used a track they deemed to be the opposite of Eaten. "We used 'Happy' by Pharrell Williams as a [comparison]," said Dr Sun. Each participant was played Happy or Eaten through headphones, while they were shown a pair of images -- one to each eye. One image showed a violent scene, such as someone being attacked in a street. The other showed something innocuous -- a group of people walking down that same street, for example. "If fans of violent music were desensitized to violence, which is what a lot of parent groups, religious groups and censorship boards are worried about, then they wouldn't show this same bias. "But the fans showed the very same bias towards processing these violent images as those who were not fans of this music."
Lead researcher Yanan Sun explained that the aim of the experiment was to measure how much participants' brains noticed violent scenes, and to compare how their sensitivity was affected by the musical accompaniment. To test the impact of different types of music, they also used a track they deemed to be the opposite of Eaten. "We used 'Happy' by Pharrell Williams as a [comparison]," said Dr Sun. Each participant was played Happy or Eaten through headphones, while they were shown a pair of images -- one to each eye. One image showed a violent scene, such as someone being attacked in a street. The other showed something innocuous -- a group of people walking down that same street, for example. "If fans of violent music were desensitized to violence, which is what a lot of parent groups, religious groups and censorship boards are worried about, then they wouldn't show this same bias. "But the fans showed the very same bias towards processing these violent images as those who were not fans of this music."
That is what statistics is for. Mathematics can tell you whether 80 is a sufficient sample size better than intuition.
Imagine instead of 80 subjects, you had a million. And suppose you managed to falsify the null hypothesis, and showed that death metal fans *do* have higher rates of desensitization than non-fans. One of two cases holds; either (a) the rate difference is too tiny to care about or (b) you could achieve a statistically significant positive result with a smaller sample size.
For this reason most well-designed social science experiments have moderate sample sizes. Experiments with a moderate number of subjects are affordable, practical, and are biased to false negatives; that means you are less likely to get statistically significant but practically insignificant results. Typical sample sizes (when they can be gotten) are in the 20-50 range. 80 is on the high end, but a *negative* result from a largish sample size is actually pretty robust. Either the differences between fans is non-existent, or it's very small, which is practically speaking the same thing.
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The joy of violence!
Its in our Nature, We are all natural born killers in some respect.
[($)]
You're a complete fraud if you think 80 models millions in a psychological human-reaction experimentation.
Minimum sample size doesn't increase linearly with population size; it asymptotically approaches a fixed value. So what you do is assume the population is arbitrarily large and size your sample accordingly. Yes, for very small populations, say hundreds, you could get away with smaller samples. But the sample size you need for a population of a million and a hundred billion aren't different at all.
The minimum sample size is *extremely* sensitive to effect size. So what you do is look up the minimum size in a table indexed by the smallest effect size you want to detect. Even if the population of the Earth has doubled since the time the table was published, the numbers are still good.
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This is what shows you are a child. My interests here is providing correct statistical information out of friendly compassion, while yours is a pissing contest with implied threats. You are goading for information you can feed to your Russian troll friends to harass people, but I doubt even on this 2019 slashdot that anyone is stupid enough to comply or immature enough to care.
For this reason most well-designed social science experiments have moderate sample sizes. Experiments with a moderate number of subjects are affordable, practical, and are biased to false negatives; that means you are less likely to get statistically significant but practically insignificant results. Typical sample sizes (when they can be gotten) are in the 20-50 range. 80 is on the high end, but a *negative* result from a largish sample size is actually pretty robust. Either the differences between fans is non-existent, or it's very small, which is practically speaking the same thing.
Most social science experiments, well actually probably the overwhelming majority, are not well-designed. Have you heard of the replication crisis?
The problem is that most social scientists do not understand mathematics, let alone statistics (a complicated subject with many caveats and nuances) very well. They rote-learn the equations and methods without fully understanding them (or understanding them at all) - I've seen in this practice.
Therefore, whenever you see a study with a sample of 80 (or a few hundred) claiming this or that, the default reaction should be extreme doubt in the results.
I've been listening to 1990s Slayer and Megadeth lately, and it not only takes me back to another time but it's really good music. Dave Mustain is awesome on guitar and Slayer's drummer is a freak of nature (I can hardly tap multiple fingers to some of his rolls). The lyrics are funny as well ("growing madness as my mind dissolves").
Good stuff. I don't like their more recent stuff though. Biased due to original listening period.
On and on south of heaven!
BlameBillCosby.com
I always assumed that Nathan Explosion was based on Johan Hegg of Amon Amarth.
My favorite metal bands:
Ensiferum (one-stop-shop for workout music)
Opeth
Ayreon
Star One
Therion
Amon Amarth
In Flames
Pantera
Megadeth
Disturbed
My eyes reflect the stars and a smile lights up my face.
Most social science experiments, well actually probably the overwhelming majority, are not well-designed.
That is true, but that can not usually be fixed with a larger sample size. If your experiment's testing procedure is bunk, then increasing the sample size is not going to help one bit. Only very, very rarely can a poorly designed experiment be saved by throwing more samples at it.
There are ten thousand different ways to get misleading results out of a poorly designed experiment. One of those ways is interpreting noise in your data as a meaningful signal, where by the barest chance you get a bunch of data points that are all weird outliers by sheer luck.
The other nine thousand nine hundred and ninety-nine ways involve introducing bias into your data -- your experimental procedure is such that you systematically are measuring the wrong thing. Perhaps your experimental subject selection procedure is such that it tends to disproportionately select the weird outliers, or perhaps the phrasing of your questions causes people to answer a different question than the one you were aiming for. This is the broad category is mistakes that social science experiments are particularly vulnerable to.
Choosing a large sample size, and other statistical methods, help avoid the error where you are measuring noise and interpreting it as a useful signal. It does not do anything whatsoever to deal with bias problems. If your experiment falls prey to one of the nine thousand nine hundred and ninety-nine mistakes where it's measuring a biased signal, then making sure you have a large number of samples will not help in the slightest. Performing your experiment with a million subjects will prove oh so definitely that you are not looking at noise -- you have measured something, all right. But that something could either be a genuine result, or the consequence of bias in your data, and to tell the difference you'll have to examine your experimental procedure in a way that has nothing to do with statistics or sample sizes.
Large sample sizes are a remedy against one specific way to ruin your experiment, out of ten thousand gotchas to watch for. It doesn't mean your procedure is sound, only that it's one mistake you didn't make. And conversely, it's not a silver bullet to avoid the other nine thousand nine hundred and ninety-nine problems either -- if your sample size is large enough to avoid the noise-based problems, then making it even larger will not help with the other gotchas.
Therefore, whenever you see a study with a sample of 80 (or a few hundred) claiming this or that, the default reaction should be extreme doubt in the results.
I don't think this follows at all.
The lesson here is that a sufficiently large corporation is indistinguishable from government. --ultranova