5. Mad Scientists

I blame bad movies. Well-meaning scientists are not going to accidentally destroy the world like in bad movies.

Yes, the Large Hadron Collider may create tiny black holes. No, they can’t consume the planet. If you calculate the size of possible black holes, and their maximum potential growth rate, you find that it would take billions of years for them to get large enough to do anything.

Yes, scientists are trying to make more intelligent machines. No, they are not going to take over the world terminator-style. Machines cannot magically leap beyond their programming the way they do in the movies. A computer cannot suddenly leap beyond its programming and become self-aware. Sorry, doesn’t happen. The fear that some scientist will create a computer that will try to destroy the human race is based on fear of change coupled with society’s overwhelming ignorance regarding how computers work. Besides, scientists have already created something that can destroy the world: it’s called a nuclear bomb.

Why it’s been inflated: Some groups trump up, just make up, the “dangers” of scientific advancement to bolster the anti-science, anti-empirical, anti-rational subculture. These are the same assholes who want creationism taught in science class.

4. Socialism

The way people talk about socialism in North America, you’d think it was a biblical plague. Socialism is just a different way of distributing wealth. There are lots of socialist countries. I’ve been to some of them. Norway, Sweden, Denmark, France – they all seem like nice places to live. No signs of the apocalypse as far as I can tell. If you think socialism is going to magically destroy humanity by reducing the centrality of greed in the politico-economic bullshitscape, you’re a tool. Go visit Norway, and then try to tell me that all those tall, healthy, attractive blond people who get free medical care and five weeks of vacation each year are the end of the world.

Why it’s been inflated: As I’ve described before, the rich have to work very hard to maintain support for capitalism among the poor, who, in most countries, have figured out that capitalism is bad for them.

3. Population movement (illegal immigration)

Nothing gets as much undo media attention as illegal immigration, especially across the Mexico/U.S. border. What the hell do you think a bunch of poor Mexicans are going to do that will bring down life as we know it? They mow lawns and pick fruit and live in fear of deportation. Leave these poor people alone and worry about the things that might actually kill you! Oh, and shut the fuck up about the border fence! Ever hear of a ladder you ignorant redneck fucktards?

Why it’s been inflated: People who take on illegal immigration do so under cover of employment statistics: ‘Illegal immigrants are stealing American jobs’ and all that. This is, of course, just the cover story. The real reason is racism pure and simple. An influx of latinos and hispanics threatens the purity of the great Aryan Klan… er, I mean race. Yes, race. Aryan race. I would argue that the same is true of resistance to immigration in many countries in europe and asia. The primary reason for resistance is making sure ‘we’ (whoever we are) maintain a controlling majority – not give up ‘our’ country’ to ‘them foreigners.’ The secondary reason is about jobs and strain on social services, but even this argument finds its genesis in the racist premise that jobs somehow ‘belong’ to members of the ruling group and that immigrants will require greater help from social services and contribute less to national productivity than the locals.

2. Economic disasters (recessions/depressions)

Whenever you hear some corporate/government douchebag agree, in principle, that the environment should be protected, but argue that now’s a bad time, what with the economy and all, HIT THEM.

Remember that scene in The Day After Tomorrow when the hero/scientist guy is trying to explain how the impossibly-fast ice age is about to glaciate all of Canada and the Continental U.S. in about 6 seconds? And then one of the big-wigs, who looks a lot like Dick Cheney, says ‘What will this do to the economy?’ WTF, right? Yeah that’s what I feel like whenever someone says we can’t have a carbon tax because it’s bad for business. Or we can’t have stricter pollution controls. Or stronger fuel efficiency standards for cars. Or areas protected from mining.

Let me put this as simply as possible. Recessions cannot kill billions of people. Environmental collapse and climate change-induced food shortages can. Fuck the economy. The economy’s not going to kill us. Ignoring environmental concerns because money is tight is barking mad.

Why it’s been inflated: Some people are just too damn thick to get their heads around the idea that their actions create reactions in their habitat, and that those reactions can and will kill them. Even if anything comes of it, they think they’ll be long dead beforehand, and they honestly don’t give a fuck about their children and grandchildren, or the fate of humanity in general. And the crazy thing is, we not only let people like this vote, but also let them run for office!

1. Terrorism

Without venturing into the absurd, the realistic worse case for terrorism is a single nuclear detonation in a major city. If optimally placed, this might kill hundreds of thousand or even a few million people. That would be bad. Extremely bad. However, it is not the end of humanity. Humanity would go on. Japan went on after Hiroshima and Nagasaki were bombed at the end of WWII. The modern world would go on after the bombing of another major city.

Compare this to bona fide EXISTENTIAL threats to humanity. Just one medium-sized asteroid impact, and the only things left would be bacteria and cockroaches.

Yet, the world spends spend trillions of dollars on terrorism and a paltry few million searching the skies and trying to figure out how stop a giant rock from ending life on earth. That’s something in the order of 100 000 times as much money going to a lesser threat as goes to an existential threat.

Why it’s been inflated: Are you familiar with Dick Cheney? Donald Rumsfeld? Governments have all kinds of reasons to inflate the threat of terrorism. It helps control the populace by stimulating blind support for the government. It creates a culture of fear that facilitates the undermining of civil rights to increase executive power. The political opposition cannot effectively convey its message without seeming down right unpatriotic. Information that would damage the administration can be hushed up ‘for national security reasons.’ And most of all, Cheney and Rumsfeld were heavily involved in defense contractors. Even when they took office (when, normally, one would have to divest themselves of all private holdings to avoid conflicts of interest) they both flatly refused, and retained stock in defense contractors (see Naomi Klein’s “The Shock Doctrine”). Inflating terrorism is about two things: money and power.

This latest bit of nonsense is called Expelled: No Intelligence Allowed, and features Stein interviewing a host of supposed academics and scientists, disgruntled because they are ostracized from academics because of their belief in a concept called ‘intelligent design,’ which, despite its name, is a theory featuring very little intelligence. Theatres showing the film are scarce (thankfully), so I have yet to see the documentary, but that’s not my concern for this week. My concern is intelligent design, and this notion that Stein and his fans have that this is somehow a valid theory worthy of merit or even consideration, when the truth is, the so-called academics who believe in it should be ostracized and laughed at. Let’s look at this ‘theory’ a little closer.

Intelligent Design vs. Evolution
Intelligent design is the belief that the development of life on Earth was guided by some intelligent being, rather than being governed by the random processes of inheritance, mutation, and natural selection. It is based strongly in a belief that god, or some incarnation of it, created the universe and has guided the development of mankind and, indeed, all species of animal on Earth. This is in opposition with the currently accepted theory of species development: neo-Darwinian evolution.

I’m going to go ahead and dumb this down, so you biologists out there, just keep quiet as I put things in layman’s terms. To the neo-Darwinian scientists, things happen something like this: cells randomly mutate all the time. Sometimes those mutations end up favorable to the life-form (i.e. beans gaining a resistance to a certain bug), and thus that life-form is able to reproduce more offspring and pass on that trait. In the same way, organisms which develop unfavorable mutations die out before they can reproduce, thus killing those bad genes off.

Theory vs. Bullshit
Neo-Darwinian evolution is what one calls a scientific ‘theory.’ Now, in common lingo, a theory can be just about any conjecture or opinion. In science, however, a theory is a testable prediction of natural phenomenon based on observable facts.

Neo-Darwinian evolution, for instance, is a theory to predict how new life-forms develop from old ones, based on facts from the fossil record and much of the work done in genetics and geological research. It is testable by looking at the fossils of connected species, and in fact, some of the more extraordinary research being done actually shows the evolution of quick-growing bacteria in action.

Here’s where we get to the big pile of smelly bullshit: intelligent design. Proponents of this theory say there’s nothing to explain where life began, and they argue that there are too many coincidences and improbabilities for evolution to occur randomly. While it is true that we have little evidence supporting any theories of how life began, deifying our own ignorance and calling it ‘god’ is not a scientific solution.

Intelligent design proponents try to replace all that is unknown with some vague deity. This does not create a valid scientific theory. This is superstition masked as science; theology pretending to be biology. God cannot be tested, seen, heard, or even smelled. To the scientist, it does not exist, as there is absolutely no evidence to support its existence.

Expelled for no Intelligence
Stein’s documentary makes the argument that academics who subscribe to intelligent design have been unfairly treated: ridiculed, not taken seriously, and even fired. I argue that they were treated fairly; they were laughed at as academics for believing in superstition.

The biologist who argues, despite all evidence to the contrary, that leprechauns are real would be laughed at. He would be ridiculed out of any legitimate conference. Why is intelligent design somehow worthy of merit, despite its basis in myth and superstition? If belief in intelligent design is accepted as science, we may as well begin teaching classes on dragons, proposing theories about the evolution of unicorns, and offering majors in Chupacabra Studies.

So, for those of you fooled by Stein’s documentary, just keep in mind what a theory is in science. Don’t accept this unfalsifiable nonsense as fact, for there is no intelligence in intelligent design. See it for what it is: superstition masquerading as biology.

Recent research presented at the International Conference on Information Systems may be of interest. Researchers June Zhu and Bernard Tan of the National University of Singapore reported on three factors that determine the effectiveness of blog advertising: blogger expertise, advertising intent and product involvement. Here is a summary of their results.

Blogger expertise refers to how much the bloggers knows about the item being advertised. Advertising intent refers to whether the recommendation of a product is explicit or implicit, for ex., “You should buy my book” is explicit; “Recently my several of my friends read the last George R. R. Martin book and really like it” is implicit. Product involvement refers to how much consideration one gives a purchase, e.g., buying popcorn at the theater is usually a low-involvement purchase, while buying a car is usually a high-involvement purchase.

The study found that almost opposite between high and low involvement products. For low-involvement products, low-expertise bloggers should be explicit and high-expertise bloggers should be implicit. In contrast, for high-involvement products, low-expertise bloggers should be implicit and it doesn’t matter if high-expertise communicators are explicit or implicit.

The full citation of the paper is as follows. Unfortunately, I couldn’t find an online version to link to.

J. Zhu and B. Tan, “Effectiveness of blog advertising: Impact of communicator expertise, advertising intent, and product involvement,” International Conference on Information Systems, Montreal, Canada, December 2007

When a paper is submitted to a good journal, a lot of smart people have at it with the academic equivalent of a howitzer. If it survives to be published, you’d think all the bugs would be worked out. Unfortunately, not all research is peer-reviewed, and reviewers tend to miss or tolerate certain weaknesses. While this is often understood by the scientific community, it can confuse the bejesus out of Joe Average.

If you’re an average person, and you need to make sense out of scientific papers, this guide is for you. If you are a science journalist and you have fewer than six graduate level research methods courses, maybe you ought to read this as well. Hell, if you got a PhD and didn’t take methods for some reason, here’s hoping this helps.

1. Is the paper in a high-quality, peer-reviewed journal?

If the “research” you have is not in a journal, be very, very wary. Sometimes good research is published in books, but be careful. The kind of research I’m talking about is done by scientists. Journalists and government officials are not scientists. Reports commissioned by government departments are (usually) not scientific.

Coming back to research published in journals, go to the journal’s webpage. Don’t worry if it’s a crappy webpage. Look for the word’s “peer-reviewed” and “acceptance rate.” If the journal is not peer reviewed or has an acceptance rate over 20%, that’s bad. You can also google the Journal’s “impact factor.” Higher is better. If you think it’s a bad journal, don’t even read the paper. If the journal is good, it means most of your work has been done for you; however, a journal’s reviewers tend to miss or tolerant certain kinds of errors that you still have to watch out for.

The same line of reasoning applies to conference proceedings. Since some fields, such as human-computer interaction, publish much of their best work in conferences, these can be excellent sources of research. However, you should only look at good, peer-reviewed conferences with low acceptance rates.

2. Who financed the study?

If whoever financed the study had something to gain from the results, don’t trust it! This is especially important in drug trials because these are often done by the drug companies who are explicitly trying to show that the drug is safe and effective. A single independent study to the contrary should be given just as much weight as all the Big Pharma studies promoting the drug combined.

Sometimes this is more subtle. A lot of research on security and drugs, for instance, is politically motivated. If a government funds a study to show that marijuana is dangerous, and the results show the opposite, things can get hairy.

3. What kind of study is it?

You have to evaluate different kinds of studies differently. Some of the kinds of studies you’re likely to encounter are: experiments, surveys, mathematical models, meta-analysis and qualitative studies. You should evaluate each of these differently.

Experiments

If the article talks about treatment groups and control groups, it’s probably an experiment. Reviewers are very good at checking that the experiment is correctly designed and the results well-interpreted, so you don’t have to worry about that. What you have to really watch out for is who participated in the study. If a drug trial was done on 100 white women, and you are a black man, the results might not apply to you. If you are a professional programmer with 20 years’ experience, the results of a study on 2nd year undergrad computer science students might not apply to you.

Surveys

If the article talks about a large number of people filling out a questionnaire online, on paper, by telephone or in person, it’s probably a survey. Reviewers are very good at making sure that the questionnaire is correctly designed and the analysis is done right, but look out for causality! Usually questionnaires argue that X causes Y, but only show that X is correlated with Y.

For instance, suppose a study claims that, for corporations, acting ethically (X) causes increased profits (Y). The study than gives evidence that a random sample of very profitable companies act more ethically than a random sample of unprofitable companies. That’s nice and all, but how do you know that it’s not the other way around? That being profitable (Y) causes the firm to act more ethically (X) because more people are watching? How about having really smart managers (Z) causes both X and Y?

When evaluating a survey that claims X causes Y, ask yourself if there are alternative explanations that the authors did not rule out.

Mathematical Models

If a paper starts with a set of assumptions and logically (usually with symbols rather than words) or mathematically derives a conclusion, I call it a mathematical model study. The good news is, you don’t have to worry much about the math or logic because the reviewers will be studying that quite closely. What you really have to watch out for are the assumptions, especially hidden assumptions.

Just read over the assumptions and think about them. Do they make sense? My favorite example is rationality. We have enormous evidence that while people may be capable of rationality, they don’t use that capability most of the time. If the paper includes assumptions that don’t hold in your case, there’s no reason to believe the paper’s results will apply to you either.

Meta-Analysis

A meta-analysis summarizes the results of many studies. Usually these are a great place to start when you’re new to a field. Unfortunately, they have one serious danger: they’re only as good as the studies they summarize. In a field with a diversity of good and bad studies, the author of a meta-analysis will usually sort out the mess for you. However, some fields, like economics and computer science, suffer from systematic methodological problems. In other words, if the whole field is screwed up, the meta-analysis probably will be as well. My only advice is, don’t just read a meta analysis.

Qualitative Studies

Qualitative research comes in many shapes and sizes. Some is presented in a highly structured way; some is written like a story. A qualitative paper describes a study within a particular context, coming to conclusions about that context, not your context. As you read the study, ask yourself how your context differs from the study’s context. After you’ve read the conclusions, ask yourself if any of the differences matter. For example, if the study is about the decision making process of a clothing retailer, and you’re in the office supplies business, the change of product may not matter.

4. Is it theory building or theory testing

Last thing you have to ask of a paper is, did the paper test a theory or merely propose one? This is usually obvious, but sometimes theory building papers masquerade as theory testing papers. As a general heuristic, put the more faith in theory testing, less faith in “exploratory studies” and very little faith in papers that propose a theory but do not give empirical evidence. While this last type is an important step in the scientific process, it’s like an experimental drug: not yet certified for human consumption.

Concluding Comments

One last thing that confuses many readers (not to mention science journalists) is the difference between “finding no evidence of a relationship” and “finding evidence of no relationship.” Journalists often write things like ‘so-and-so concludes that drug X is not effective.’ This is almost never correct. Very few scientific papers ever conclude that two things are unrelated (e.g., a drug doesn’t work). Experiments and surveys just aren’t set up that way.

I hope this guide helps you make sense of scientific papers.