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12.1: Effect-size and power

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    7961
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    If you already know something about statistics while you were reading this book, you might have noticed that we neglected to discuss the topic of effect-size, and we barely talked about statistical power. We will talk a little bit about these things here.

    First, it is worth pointing out that over the years, at least in Psychology, many societies and journals have made recommendations about how researchers should report their statistical analyses. Among the recommendations is that measures of “effect size” should be reported. Similarly, many journals now require that researchers report an “a priori” power-analysis (the recommendation is this should be done before the data is collected). Because these recommendations are so prevalent, it is worth discussing what these ideas refer to. At the same time, the meaning of effect-size and power somewhat depend on your “philosophical” bent, and these two ideas can become completely meaningless depending on how you think of statistics. For these complicating reasons we have suspended our discussion of the topic until now.

    The question or practice of using measures of effect size and conducting power-analyses are also good examples of the more general need to think about about what you are doing. If you are going to report effect size, and conduct power analyses, these activities should not be done blindly because someone else recommends that you do them, these activities and other suitable ones should be done as a part of justifying what you are doing. It is a part of thinking about how to make your data answer questions for you.

     

    Chance vs. real effects

    Let’s rehash something we’ve said over and over again. First, researchers are interested in whether their manipulation causes a change in their measurement. If it does, they can become confident that they have uncovered a causal force (the manipulation). However, we know that differences in the measure between experimental conditions can arise by chance alone, just by sampling error. In fact, we can create pictures that show us the window of chance for a given statistic, these tells us roughly the range and likelihoods of getting various differences just by chance. With these windows in hand, we can then determine whether the differences we found in some data that we collected were likely or unlikely to be due to chance. We also learned that sample-size plays a big role in the shape of the chance window. Small samples give chance a large opportunity make big differences. Large samples give chance a small opportunity to make big differences. The general lesson up to this point has been, design an experiment with a large enough sample to detect the effect of interest. If your design isn’t well formed, you could easily be measuring noise, and your differences could be caused by sampling error. Generally speaking, this is still a very good lesson: better designs produce better data; and you can’t fix a broken design with statistics.

    There is clearly another thing that can determine whether or not your differences are due to chance. That is the effect itself. If the manipulation does cause a change, then there is an effect, and that effect is a real one. Effects refer to differences in the measurement between experimental conditions. The thing about effects is that they can be big or small, they have a size.

    For example, you can think of a manipulation in terms of the size of its hammer. A strong manipulation is like a jack-hammer: it is loud, it produces a big effect, it creates huge differences. A medium manipulation is like regular hammer: it works, you can hear it, it drives a nail into wood, but it doesn’t destroy concrete like a jack-hammer, it produces a reliable effect. A small manipulation is like tapping something with a pencil: it does something, you can barely hear it, and only in a quiet room, it doesn’t do a good job of driving a nail into wood, and it does nothing to concrete, it produces tiny, unreliable effects. Finally, a really small effect would be hammering something with a feather, it leaves almost no mark and does nothing that is obviously perceptiple to nails or pavement. The lesson is, if you want to break up concrete, use a jack-hammer; or, if you want to measure your effect, make your manipulation stronger (like a jack-hammer) so it produces a bigger difference.

     

    Effect size: concrete vs. abstract notions

    Generally speaking, the big concept of effect size, is simply how big the differences are, that’s it. However, the biggness or smallness of effects quickly becomes a little bit complicated. On the one hand, the raw difference in the means can be very meaningful. Let’s saw we are measuring performance on a final exam, and we are testing whether or not a miracle drug can make you do better on the test. Let’s say taking the drug makes you do 5% better on the test, compared to not taking the drug. You know what 5% means, that’s basically a whole letter grade. Pretty good. An effect-size of 25% would be even better right! Lot’s of measures have a concrete quality to them, and we often want to the size of the effect expressed in terms of the original measure.

    Let’s talk about concrete measures some more. How about learning a musical instrument. Let’s say it takes 10,000 hours to become an expert piano, violin, or guitar player. And, let’s say you found something online that says that using their method, you will learn the instrument in less time than normal. That is a claim about the effect size of their method. You would want to know how big the effect is right? For example, the effect-size could be 10 hours. That would mean it would take you 9,980 hours to become an expert (that’s a whole 10 hours less). If I knew the effect-size was so tiny, I wouldn’t bother with their new method. But, if the effect size was say 1,000 hours, that’s a pretty big deal, that’s 10% less (still doesn’t seem like much, but saving 1,000 hours seems like a lot).

    Just as often as we have concrete measures that are readily interpretable, Psychology often produces measures that are extremely difficult to interpret. For example, questionnaire measures often have no concrete meaning, and only an abstract statistical meaning. If you wanted to know whether a manipulation caused people to more or less happy, and you used to questionnaire to measure happiness, you might find that people were 50 happy in condition 1, and 60 happy in condition 2, that’s a difference of 10 happy units. But how much is 10? Is that a big or small difference? It’s not immediately obvious. What is the solution here? A common solution is to provide a standardized measure of the difference, like a z-score. For example, if a difference of 10 reflected a shift of one standard deviation that would be useful to know, and that would be a sizeable shift. If the difference was only a .1 shift in terms of standard deviation, then the difference of 10 wouldn’t be very large. We elaborate on this idea next in describing cohen’s d.

     

    Cohen’s d

    Let’s look a few distributions to firm up some ideas about effect-size. In the graph below you will see four panels. The first panel (0) represents the null distribution of no differences. This is the idea that your manipulation (A vs. B) doesn’t do anything at all, as a result when you measure scores in conditions A and B, you are effectively sampling scores from the very same overall distribution. The panel shows the distribution as green for condition B, but the red one for condition A is identical and drawn underneath (it’s invisible). There is 0 difference between these distributions, so it represent a null effect.

    library(ggplot2)
    X<-c(seq(-5,5,.1),seq(-5,5,.1),
         seq(-5,5,.1),seq(-5,5,.1),
         seq(-5,5,.1),seq(-5,5,.1),
         seq(-5,5,.1),seq(-5,5,.1))
    Y<-c(dnorm(seq(-5,5,.1),0,1),dnorm(seq(-5,5,.1),0,1),
         dnorm(seq(-5,5,.1),0,1),dnorm(seq(-5,5,.1),.5,1),
         dnorm(seq(-5,5,.1),0,1),dnorm(seq(-5,5,.1),1,1),
         dnorm(seq(-5,5,.1),0,1),dnorm(seq(-5,5,.1),2,1))
    effect_size<-rep(c(0,.5,1,2),each=101*2)
    condition<-rep(rep(c("A","B"),each=101),2)
    df<-data.frame(effect_size,
                   condition,
                   X,Y)
    ggplot(df, aes(x=X,y=Y, group=condition, color=condition))+
      geom_line()+
      theme_classic(base_size = 15)+
      facet_wrap(~effect_size)+
      xlab("values")+
      ylab("density")
    Figure \(\PageIndex{1}\): Each panel shows hypothetical distributions for two conditions. As the effect-size increases, the difference between the distributions become larger.

    The remaining panels are hypothetical examples of what a true effect could look like, when your manipulation actually causes a difference. For example, if condition A is a control group, and condition B is a treatment group, we are looking at three cases where the treatment manipulation causes a positive shift in the mean of distribution. We are using normal curves with mean =0 and sd =1 for this demonstration, so a shift of .5 is a shift of half of a standard deviation. A shift of 1 is a shift of 1 standard deviation, and a shift of 2 is a shift of 2 standard deviations. We could draw many more examples showing even bigger shifts, or shifts that go in the other direction.

    Let’s look at another example, but this time we’ll use some concrete measurements. Let’s say we are looking at final exam performance, so our numbers are grade percentages. Let’s also say that we know the mean on the test is 65%, with a standard deviation of 5%. Group A could be a control that just takes the test, Group B could receive some “educational” manipulation designed to improve the test score. These graphs then show us some hypotheses about what the manipulation may or may not be doing.

    library(ggplot2)
    X<-c(seq(25,100,1),seq(25,100,1),
         seq(25,100,1),seq(25,100,1),
         seq(25,100,1),seq(25,100,1),
         seq(25,100,1),seq(25,100,1))
    Y<-c(dnorm(seq(25,100,1),65,5),dnorm(seq(25,100,1),65,5),
         dnorm(seq(25,100,1),65,5),dnorm(seq(25,100,1),67.5,5),
         dnorm(seq(25,100,1),65,5),dnorm(seq(25,100,1),70,5),
         dnorm(seq(25,100,1),65,5),dnorm(seq(25,100,1),75,5))
    effect_size<-rep(c("65, d=0","67.5,d=.5","70, d=1","75, d=2"),each=76*2)
    condition<-rep(rep(c("A","B"),each=76),2)
    df<-data.frame(effect_size,
                   condition,
                   X,Y)
    ggplot(df, aes(x=X,y=Y, group=condition, color=condition))+
      geom_line()+
      theme_classic(base_size = 15)+
      facet_wrap(~effect_size)+
      xlab("values")+
      ylab("density")
    Figure \(\PageIndex{2}\): Each panel shows hypothetical distributions for two conditions. As the effect-size increases, the difference between the distributions become larger.

    The first panel shows that both condition A and B will sample test scores from the same distribution (mean =65, with 0 effect). The other panels show shifted mean for condition B (the treatment that is supposed to increase test performance). So, the treatment could increase the test performance by 2.5% (mean 67.5, .5 sd shift), or by 5% (mean 70, 1 sd shift), or by 10% (mean 75%, 2 sd shift), or by any other amount. In terms of our previous metaphor, a shift of 2 standard deviations is more like jack-hammer in terms of size, and a shift of .5 standard deviations is more like using a pencil. The thing about research, is we often have no clue about whether our manipulation will produce a big or small effect, that’s why we are conducting the research.

    You might have noticed that the letter \(d\) appears in the above figure. Why is that? Jacob Cohen used the letter \(d\) in defining the effect-size for this situation, and now everyone calls it Cohen’s \(d\). The formula for Cohen’s \(d\) is:

    \[d = \frac{\text{mean for condition 1} - \text{mean for condition 2}}{\text{population standard deviation}} \nonumber \]

    If you notice, this is just a kind of z-score. It is a way to standardize the mean difference in terms of the population standard deviation.

    It is also worth noting again that this measure of effect-size is entirely hypothetical for most purposes. In general, researchers do not know the population standard deviation, they can only guess at it, or estimate it from the sample. The same goes for means, in the formula these are hypothetical mean differences in two population distributions. In practice, researchers do not know these values, they guess at them from their samples.

    Before discussing why the concept of effect-size can be useful, we note that Cohen’s \(d\) is useful for understanding abstract measures. For example, when you don’t know what a difference of 10 or 20 means as a raw score, you can standardize the difference by the sample standard deviation, then you know roughly how big the effect is in terms of standard units. If you thought a 20 was big, but it turned out to be only 1/10th of a standard deviation, then you would know the effect is actually quite small with respect to the overall variability in the data.


    12.1: Effect-size and power is shared under a CC BY-SA 4.0 license and was authored, remixed, and/or curated by Matthew J. C. Crump via source content that was edited to conform to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.