Computer guide for psych majors

Computer guide for psych majors

If you're about to be a psych major, here's your handy tech guide! Spoiler alert: you won't need an expensive computer.

If you're going to grad school for psychology, your needs may be a bit different...but chances are good that the recommendations here will suffice for you as well.

Thinking about the GRE?

Thinking about grad school and the GRE?

You'll find some review materials available here.

Up-to-date information about the test itself, the nearest testing center to you, registration for the test, and more is available here.

• Start preparing for the GRE over winter break of your junior year, at the latest. Take practice tests, to get familiar with the kinds of questions on the exam and the time limits you'll face.
  
  Prepare more intensively in the months before you take the exam. There are free materials available online, or at your local library—it's not necessary to spend $30+ on a prep book.
• If you're particularly bad at taking tests, you may want to begin practicing even earlier. Many programs, particularly academic-focused (as opposed to career-oriented) programs, assume as a matter of course that you'll do well on the GRE.
  
  For certain graduate programs, like a PhD in Creative Writing, your score on the Quantitative sections isn't so important; likewise, your Analytical Writing score holds less weight with a PhD program in Mathematics or Physics. If your goal is to attend a Master's program and enter the workforce directly afterward, the expectations for your GRE score will be a bit lower than the expectations of a PhD program.

• You'll want to take the GRE before the beginning of your final year in college—if you take it during the year, when you're taking a full load of classes, you'll be too stressed out to do your best. You'll also have to take a trip to the nearest specialized exam center (if you're in the U.S.).

• More importantly, carefully pick the programs you're applying to. You pay $195 to take the test. On the day of the test, you can select 4 schools to receive your score; know where you want to send your GRE results.
  
  To send your score to any additional universities, you will have to pay $27 per school (and it may cost more than that now; prices only go up).

How do you know which schools you want to apply to? Pick your particular area of interest, and know which professors you'd like to work with. Think of it less as applying to a particular school, and more as an application to work with a particular professor (or a particular set of professors).

Know the acceptance rate of each program you apply to, and to improve your odds of acceptance, be sure that they're not all top-tier, hyper-competitive programs.

It's often helpful to get some relevant experience in that field, and if you get an interview with one of the professors you'd like to work with, be prepared to discuss your interest—as well as your research ideas—with someone who already has a PhD in the field.

• A subpar GRE score is not necessarily going to ruin your chances of admission to a graduate program, but it will decrease your odds (especially if you plan to apply to top-tier programs). If you don't do so well, it may be worth addressing (briefly) in your academic statement of purpose or your personal statement.
  
  If you get a disappointing score, resist the urge to explain it away, even if you have a legitimate reason for your low score (e.g. "I had been sick for several days before the test," or "My neighbors had a loud party that ruined my sleep the night before the test")—it will appear that you're making excuses, and that probably won't go over so well.
  
  You're better off presenting a poor score in a positive light, for example, "My GRE score may not have been impressive, but a top-notch work ethic, hunger for success, and passion for the field have always allowed me to overcome any limitations I've faced." It won't always work, but if you have relevant experience and a strong GPA, a potential advisor may be willing to overlook a substandard GRE score.

Sound scary? It is. It is wise to start thinking about this process early; it'll make your life a lot easier when you've planned this process in advance. Start preparing for the GRE early, and also go into the testing center knowing which schools you want to apply to.

The part of the grad school admissions process that will take the most time and effort is identifying schools that AREN'T top-tier or very well-known...but that is the research that is most likely to pay off! The chances of admission are much greater at Middle Of Nowhere University than at a school like Harvard or Princeton.

If you think your record is strong enough, you're certainly welcome to apply to top programs in your field—but just remember that if it's a highly-ranked school, everyone will apply there, so the chance that a professor actually looks at your application is much slimmer than at a less-competitive program. Hedge your bets by applying to both types of schools.

What You Think You Know About Psychology is Wrong

What You Think You Know About Psychology is Wrong:

The limitations of null hypothesis significance testing

By: Zach Basehore

Are college students psychic?!

• http://laist.com/2015/07/29/hollywood_teen_medium_show.php
• http://talk.collegeconfidential.com/college-life/1453229-what-is-a-good-college-major-for-a-psychic.html

[Or maybe they're just really unlucky: http://www.deirdreverne.com/on-behalf-of-college-students-a-professor-appeals-to-psychic-theresa-caputo-the-long-island-medium/]

Let's say someone claims that people who go to college are more psychic than people who do not attend college. So I decide to test this claim!

How would I do that? Well, a simple test would be to examine people's ability to correctly predict whether a coin will land on heads or tails when I flip it. There are 10,000 college students and 10,000 non-college students; each person predicts the results of 100,000 coin flips, one flip at a time.

The results:

Each participant had a proportion of correct predictions. The mean proportion of correct predictions among college students was .50006 (that is, 50.006% correct), and the non-college-students had a mean proportion of correct predictions equal to .49999. The SDs are .00160 and .00155, respectively.

When you run an independent-samples t test, this difference is statistically significant at an alpha level of .01! The 95% CI for the difference is also quite narrow (indicating that these means are very close to the true population means).

So the statistical test gives us very strong evidence that college students really are more prescient than non-college students! We've made a new discovery that revolutionizes our understanding of the human mind, and opens up a whole new field of inquiry! Why are college students more psychic? Is it because they're smarter? More sensitive? Do they pay closer attention to the world around them?

The problem:

In this example, I've found evidence of psychic abilities! Specifically, I've shown that college students predict the outcome of coin flips more accurately than non-college students, and there's less than a 1% probability that the difference I found is due to chance alone, if the null hypothesis is true at the population level)! How exciting—I can establish a huge name for myself among scientific psychologists, and have my pick of schools at which to continue my groundbreaking research! I could continue this research at Oxford… nah, let's find a better climate; like Miami or USC. I could get multi-million-dollar grants to fund an elaborate lab with fancy equipment! I can give TED talks, write books and go on lucrative speaking tours...my research will grab headlines the world over! I’ll be a household name!

The gut-check:

But wait a second...what was the actual difference again? On average, college students are right on 7 more trials (out of 100,000) than non-college students?...

Any time you gather real-world data, you’d expect there to be some small difference between groups, even if it’s really not due to any systematic effect. In the research described above, everything happened in just the right way to give me a spurious result:

• 1 - low variance within each group [thanks in part to the excessive sample size; see the law of large numbers];
• 2 - a small but statistically significant difference that can easily be explained by a seemingly reasonable mechanism, and
• 3 - a very large sample.

These factors explain how I found a statistically significant difference between college students and non-college students despite the tiny difference in means.

Excited by the significant result and the potential to trumpet my exciting new ‘discovery’ [thereby launching a career, positioning myself as an expert who can charge ridiculously high consulting or speaking fees], I've failed to critically evaluate the implications of my results. And therefore, I've failed as a scientist. :(

How can we avoid falling into that trap?

One solution:

A standardized measure of effect size, like Cohen's d, will reveal what SHOULD be obvious from a look at the raw data: this difference between groups is tiny and practically insignificant, and it shouldn't convince anyone that college students are actually psychic!

In the spirit of scientific inquiry, you can test this for yourself! At GraphPad QuickCalcs, enter a mean of .50006 for Group 1 and .49999 for Group 2. Next, enter the SD of .00160 for Group 1 and .00155 for Group 2. The N for each group is 10000. Hit "Calculate now" and see what you get.

Now, enter the same means and SDs, but change the N to 100 for both groups, and observe the results.

Then, go to the Cohen's d calculator here and enter the same information (it doesn't ask for sample size). So what does all of this information mean?…

I’ve already done the easy part for you:

Sample of 20,000:

Sample of 200:

Cohen's d:

***

Statisical significance is a concept that has been called idolatrous, mindless, and an educational failure that proves essentially nothing! But every psychology major and minor has to learn it nonetheless...

The absurd focus on p-values in many social science fields (like psychology, education, economics, and biomedical science) leads to articles like the highly influential John Ioannidis piece Why Most Published Research Findings Are False—which has been cited over 4000 times! 

A variety of ridiculous conclusions have been published based on small p-values, such as:

• The assertion that our behavior changes due to existential crisis at the end of every decade of life (e.g. age 29, 39, 49, etc.)—when even a cursory glance at the data shows otherwise!
• That college students can detect erotic images before they appear on a computer screen!
• That hurricanes with female names are deadlier because people are less afraid of hurricanes with nice-sounding female names than of hurricanes with sterner-sounding male names, so fewer people leave—therefore leading to more deaths.
  

  • See this or this for devastating criticisms of this superficially plausible piece.
• Or that people are actually younger after listening to “When I'm Sixty-Four” by the Beatles than an instrumental control song!
  

  • The linked article actually protests current publication practices and researcher degrees of freedom; that is, the different ex post facto decisions that researchers often make in order to obtain statistically significant differences.

This is exactly why I pound the figurative table so hard about using effect sizes and well-designed, targeted experimental research. Don't just run NHST procedures on autopilot, or collect a huge dataset and mine for significance, or draw conclusions based solely on the arbitrary p ≤ .05 standard.

“But that's not how math works! How is the .05 standard arbitrary? And where did it come from?” Well, Gigerenzer (2004) identifies the source of this practice as a 1935 textbook by the influential psychological statistician Sir R.A. Fisher—and Gigerenzer also notes that Fisher himself wrote in 1956 that the practice of always relying on the .05 standard is “absurdly academic” and is not useful for scientific inquiry!

So, one of the early thinkers on whose work current psychological statistical practice is based would likely recoil in horror at what has become of statistical practice in our field today! [Note, however, that Cowles and Davis (1982) identified similar, though less absolute, rules about an older statistical practice called ‘probable error.’]

Remember that the greatest scientific discoveries, such as gravity, the laws of thermodynamics, Darwin's description of natural selection, and Pavlov's discovery of classical condition—not one relied on anything like p-values. 

Not. 

One.

There is truly no substitute—none whatsoever—for thinking critically about the quality of your research design, the strengths and limitations of your procedure, and the size and replicability of your effect. Attempts to automate interpretation based on the .05 standard (or any such universal line-in-the-sand!) result in most researchers pumping out mounds of garbage and hoping to find a diamond in the rubbish heap, rather than setting out specifically to find a genuine diamond...

Conclusion? The validity of most psychological research is questionable (at best)! We're taught to base research around statistical procedures that are of dubious help in understanding a phenomenon—and our work is almost always published solely on that basis! This pervasive problem will not be easy to fix: we need the entire field to stop doing analyses on autopilot, and to start thinking deeply and critically!

The most powerful evidence is, and will always be, to show that an effect occurs over, and over, and over again.

*** 

If you need further explanations, here are a couple helpful links:

• Explanation of effect size: https://www.leeds.ac.uk/educol/documents/00002182.htm
• Excellent slides on the p-value: http://www.slideshare.net/pbbarlow1/whats-significant-hypothesis-testing-effect-size-confidence-intervals-the-pvalue-fallacy

Some interesting links on the investigation of people who claim to have paranormal powers:

• https://en.wikipedia.org/wiki/One_Million_Dollar_Paranormal_Challenge
• http://www.csicop.org/si/show/fakers_and_innocents_the_one_million_dollar_challenge_and_those_who_try_for
• https://www.youtube.com/watch?v=M9w7jHYriFo