Tag Archives: power

Correlations in neuroscience: are small n, interaction fallacies, lack of illustrations and confidence intervals the norm?

As reviewer, editor and reader of research articles, I’m regularly annoyed by the low standards in correlation analyses. In my experience with such articles, typically:

  • Pearson’s correlation, a non-robust measure of association, is used;
  • R and p values are reported, but not confidence intervals;
  • sample sizes tend to be small, leading to large estimation bias and inflated effect sizes in the literature;
  • R values and confidence intervals are not considered when interpreting the results;
  • instead, most analyses are reported as significant or non-significant (p<0.05), leading to the conclusion that an association exists or not (frequentist fallacy);
  • often figures illustrating the correlations are absent;
  • the explicit or implicit comparison of two correlations is done without a formal test (interaction fallacy).

To find out if my experience was in fact representative of the typical paper, I had a look at all papers published in 2017 in the European Journal of Neuroscience, where I’m a section editor. I care about the quality of the research published in EJN, so this is not an attempt at blaming a journal in particular, rather it’s a starting point to address a general problem. I really hope the results presented below will serve as a wake-up call for all involved and will lead to improvements in correlation analyses. Also, I bet if you look systematically at articles published in other neuroscience journals you’ll find the same problems. If you’re not convinced, go ahead, prove me wrong 😉 

I proceeded like this: for all 2017 articles (volumes 45 and 46), I searched for “correl” and I scanned for figures of scatterplots. If either searches were negative, the article was categorised as not containing a correlation analysis, so I might have missed a few. When at least one correlation was present, I looked for these details: 

  • n
  • estimator
  • confidence interval
  • R
  • p value
  • consideration of effect sizes
  • figure illustrating positive result
  • figure illustrating negative result
  • interaction test.

164 articles reported no correlation.

7 articles used regression analyses, with sample sizes as low as n=6, n=10, n=12 in 3 articles.

48 articles reported correlations.

Sample size

The norm was to not report degrees of freedom or sample size along with the correlation analyses or their illustrations. In 7 articles, the sample sizes were very difficult or impossible to guess. In the others, sample sizes varied a lot, both within and between articles. To confirm sample sizes, I counted the observations in scatterplots when they were available and not too crowded – this was a tedious job and I probably got some estimations and checks wrong. Anyway, I shouldn’t have to do all these checks, so something went wrong during the reviewing process. 

To simplify the presentation of the results, I collapsed the sample size estimates across articles. Here is the distribution: 

figure_ejn_sample_sizes

The figure omits 3 outliers with n= 836, 1397, 1407, all from the same article.

The median sample size is 18, which is far too low to provide sufficiently precise estimation.

Estimator

The issue with low sample sizes is made worse by the predominant use of Pearson’s correlation or the lack of consideration for the type of estimator. Indeed, 21 articles did not mention the estimator used at all, but presumably they used Pearson’s correlation.

Among the 27 articles that did mention which estimator was used:

  • 11 used only Pearson’s correlation;
  • 11 used only Spearman’s correlation;
  • 4 used Pearson’s and Spearman’s correlations;
  • 1 used Spearman’s and Kendall’s correlations.

So the majority of studies used an estimator that is well-known for its lack of robustness and its inaccurate confidence intervals and p values (Pernet, Wilcox & Rousselet, 2012).

R & p values

Most articles reported R and p values. Only 2 articles did not report R values. The same 2 articles also omitted p values, simply mentioning that the correlations were not significant. Another 3 articles did not report p values along with the R values.

Confidence interval

Only 3 articles reported confidence intervals, without mentioning how they were computed. 1 article reported percentile bootstrap confidence intervals for Pearson’s correlations, which is the recommended procedure for this estimator (Pernet, Wilcox & Rousselet, 2012).

Consideration for effect sizes

Given the lack of interest for measurement uncertainty demonstrated by the absence of confidence intervals in most articles, it is not surprising that only 5 articles mentioned the size of the correlation when presenting the results. All other articles simply reported the correlations as significant or not.

Illustrations

In contrast with the absence of confidence intervals and consideration for effect sizes, 23 articles reported illustrations for positive results. 4 articles reported only negative results, which leaves us with 21 articles that failed to illustrate the correlation results. 

Among the 40 articles that reported negative results, only 13 illustrated them, which suggests a strong bias towards positive results.

Interaction test

Finally, I looked for interaction fallacies (Nieuwenhuis, Forstmann & Wagenmakers 2011). In the context of correlation analyses, you commit an interaction fallacy when you present two correlations, one significant, the other not, implying that the 2 differ, but without explicitly testing the interaction. In other versions of the interaction fallacy, two significant correlations with the same sign are presented together, implying either that the 2 are similar, or that one is stronger than the other, without providing a confidence interval for the correlation difference. You can easily guess the other flavours… 

10 articles presented only one correlation, so there was no scope for the interaction fallacy. Among the 38 articles that presented more than one correlation, only one provided an explicit test for the comparison of 2 correlations. However, the authors omitted the explicit test for their next comparison!

Recommendations

In conclusion, at least in 2017 EJN articles, the norm is to estimate associations using small sample sizes and a non-robust estimator, to not provide confidence intervals and to not consider effect sizes and measurement uncertainty when presenting the results. Also, positive results are more likely to be illustrated than negative ones. Finally, interaction fallacies are mainstream.

How can we do a better job?

If you want to do a correlation analysis, consider your sample size carefully to assess statistical power and even better, your long-term estimation precision. If you have a small n, I wouldn’t even look at the correlation. 

Do not use Pearson’s correlation unless you have well-behaved and large samples, and you are only interested in linear relationships; otherwise explore robust measures of associations and techniques that provide valid confidence intervals (Pernet, Wilcox & Rousselet, 2012; Wilcox & Rousselet, 2018).

Reporting

These details are essential in articles reporting correlation analyses:

  • sample size for each correlation;
  • estimator of association;
  • R value;
  • confidence interval;
  • scatterplot illustration of every correlation, irrespective of the p value;
  • explicit comparison test of all correlations explicitly or implicitly compared;
  • consideration of effect sizes (R values) and their uncertainty (confidence intervals) in the interpretation of the results.

 Report p values if you want but they are not essential and should not be given a special status (McShane et al. 2018).

Finally, are you sure you really want to compute a correlation?

“Why then are correlation coefficients so attractive? Only bad reasons seem to come to mind. Worst of all, probably, is the absence of any need to think about units for either variable. Given two perfectly meaningless variables, one is reminded of their meaninglessness when a regression coefficient is given, since one wonders how to interpret its value. A correlation coefficient is less likely to bring up the unpleasant truth—we think we know what r = —.7 means. Do we? How often? Sweeping things under the rug is the enemy of good data analysis. Often, using the correlation coefficient is “sweeping under the rug” with a vengeance. Being so disinterested in our variables that we do not care about their units can hardly be desirable.”
Analyzing data: Sanctification or detective work?

John W. Tukey.
 American Psychologist, Vol 24(2), Feb 1969, 83-91. http://dx.doi.org/10.1037/h0027108

 

References

McShane, B.B., Gal, D., Gelman, A., Robert, C. & Tackett, J.L. (2018) Abandon Statistical Significance. arxiv.

Nieuwenhuis, S., Forstmann, B.U. & Wagenmakers, E.J. (2011) Erroneous analyses of interactions in neuroscience: a problem of significance. Nat Neurosci, 14, 1105-1107.

Pernet, C.R., Wilcox, R. & Rousselet, G.A. (2012) Robust correlation analyses: false positive and power validation using a new open source matlab toolbox. Front Psychol, 3, 606.

Rousselet, G.A. & Pernet, C.R. (2012) Improving standards in brain-behavior correlation analyses. Frontiers in human neuroscience, 6, 119.

Wilcox, R.R. & Rousselet, G.A. (2018) A Guide to Robust Statistical Methods in Neuroscience. Curr Protoc Neurosci, 82, 8 42 41-48 42 30.

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Your power is lower than you think

The previous post considered alpha when sampling from normal and non-normal distributions. Here the simulations are extended to look at power in the one-sample case. Statistical power is the long term probability of returning a significant test when there is an effect, or probability of true positives.

Power depends both on sample size and effect size. This is illustrated in the figure below, which reports simulations with 5,000 iterations, using t-test on means applied to samples from a normal distribution.

power_mean

Now, let’s look at power in less idealistic conditions, for instance when sampling from a lognormal distribution, which is strongly positively skewed. This power simulation used 10,000 iterations and an effect size of 0.5, i.e. we sample from distributions that are shifted by 0.5 from zero.

power

Under normality (dashed lines), as expected the mean performs better than the 20% trimmed mean and the median: we need smaller sample sizes to reach 80% power when using the mean. However, when sampling from a lognormal distribution the performance of the three estimators is completely reversed: now the mean performs worse; the 20% trimmed mean performs much better; the median performs even better. So when sampling from a skewed distribution, the choice of statistical test can have large effects on power. In particular, a t-test on the mean can have very low power, whereas a t-test on a trimmed mean, or a test on the median can provide much larger power.

As we did in the previous post, let’s look at power in different situations in which we vary the asymmetry and the tails of the distributions. The effect size is 0.5.

Asymmetry manipulation

A t-test on means performs very well under normality (g=0), as we saw in the previous figure. However, as asymmetry increases, power is strongly affected. With large asymmetry (g>1) the t-test is biased: starting from very low sample sizes, power goes down with increasing sample sizes, before going up again in some situations.

figure_power_g_mean

A t-test using a 20% trimmed mean is dramatically less affected by asymmetry than the mean.

figure_power_g_tmean

The median also performs much better than the mean but it behaves differently from the mean and the 20% trimmed mean: power increases with increasing asymmetry!

figure_power_g_median

Tail manipulation

What happens when we manipulate the tails instead? Remember that samples from distributions with heavy tails tend to contain outliers, which affect disproportionally the mean and the variance compared to robust estimators. Not surprisingly, t-tests on means are strongly affected by heavy tails.

figure_power_h_mean

The 20% trimmed mean boosts power significantly, although it is still affected by heavy tails.

figure_power_h_tmean

The median performs the best, showing very limited power drop with increasing tail thickness.

figure_power_h_median

Conclusion

The simulations presented here are of course limited, but they serve as a reminder that power should be estimated using realistic distributions, for instance if the goal is to estimate well-known skewed distributions such as reaction times. The choice of estimators is also critical, and it would be wise to consider robust estimators whenever appropriate.

References

Wilcox, R.R. (2012) Introduction to robust estimation and hypothesis testing. Academic Press, San Diego, CA.

Wilcox, Rand; Rousselet, Guillaume (2017): A guide to robust statistical methods in neuroscience. figshare. https://doi.org/10.6084/m9.figshare.5114275.v1

Trimmed means

The R code for this post is on github.

Trimmed means are robust estimators of central tendency. To compute a trimmed mean, we remove a predetermined amount of observations on each side of a distribution, and average the remaining observations. If you think you’re not familiar with trimmed means, you already know one famous member of this family: the median. Indeed, the median is an extreme trimmed mean, in which all observations are removed except one or two.

Using trimmed means confers two advantages:

  • trimmed means provide a better estimation of the location of the bulk of the observations than the mean when sampling from asymmetric distributions;
  • the standard error of the trimmed mean is less affected by outliers and asymmetry than the mean, so that tests using trimmed means can have more power than tests using the mean.

Important point: if we use a trimmed mean in an inferential test (see below), we make inferences about the population trimmed mean, not the population mean. The same is true for the median or any other measure of central tendency. So each robust estimator is a tool to answer a specific question, and this is why different estimators can return different answers…

Here is how we compute a 20% trimmed mean.

Let’s consider a sample of 20 observations:

39 92 75 61 45 87 59 51 87 12  8 93 74 16 32 39 87 12 47 50

First we sort them:

8 12 12 16 32 39 39 45 47 50 51 59 61 74 75 87 87 87 92 93

The number of observations to remove is floor(0.2 * 20) = 4. So we trim 4 observations from each end:

(8 12 12 16) 32 39 39 45 47 50 51 59 61 74 75 87 (87 87 92 93)

And we take the mean of the remaining observations, such that our 20% trimmed mean = mean(c(32,39,39,45,47,50,51,59,61,74,75,87)) = 54.92

Let’s illustrate the trimming process with a normal distribution and 20% trimming:

normdist

We can see how trimming gets rid of the tails of the distribution, to focus on the bulk of the observations. This behaviour is particularly useful when dealing with skewed distributions, as shown here:

fdist

In this skewed distribution (it’s an F distribution), there is more variability on the right side, which appears as stretched compared to the left side. Because we trim the same amount on each side, trimming removes a longer chunk of the distribution on the right side than the left side. As a consequence, the mean of the remaining points is more representative of the location of the bulk of the observations. This can be seen in the following examples.

figure_tm_demo

Panel A shows the kernel density estimate of 100 observations sampled from a standard normal distribution (MCT stands for measure of central tendency). By chance, the distribution is not perfectly symmetric, but the mean, 20% trimmed mean and median give very similar estimates, as expected. In panel B, however, the sample is from a lognormal distribution. Because of the asymmetry of the distribution, the mean is dragged towards the right side of the distribution, away from the bulk of the observations. The 20% trimmed mean is to the left of the mean, and the median further to the left, closer to the location of most observations. Thus, for asymmetric distributions, trimmed means provide more accurate information about central tendency than the mean.

**Q: “By trimming, don’t we loose information?”**

I have heard that question over and over. The answer depends on your goal. Statistical methods are only tools to answer specific questions, so it always depends on your goal. I have never met anyone with a true interest in the mean: the mean is always used, implicitly or explicitly, as a tool to indicate the location of the bulk of the observations. Thus, if your goal is to estimate central tendency, then no, trimming doesn’t discard information, it actually increases the quality of the information about central tendency.

I have also heard that criticism: “I’m interested in the tails of the distributions and that’s why I use the mean, trimming gets rid of them”. Tails certainly have interesting stories to tell, but the mean is absolutely not the tool to study them because it mingles all observations into one value, so we have no way to tell why means differ among samples. If you want to study entire distributions, they are fantastic graphical tools available (Rousselet, Pernet & Wilcox 2017).

Implementation

Base R has trimmed means built in:

mean can be used by changing the trim argument to the desired amount of trimming:

mean(x, trim = 0.2) gives a 20% trimmed mean.

In Matlab, try the tm function available here.

In Python, try the scipy.stats.tmean function. More Python functions are listed here.

Inferences

There are plenty of R functions using trimmed means on Rand Wilcox’s website.

We can use trimmed means instead of means in t-tests. However, the calculation of the standard error is different from the traditional t-test formula. This is because after trimming observations, the remaining observations are no longer independent. The formula for the adjusted standard error was originally proposed by Karen Yuen in 1974, and it involves winsorization. To winsorize a sample, instead of removing observations, we replace them with the remaining extreme values. So in our example, a 20% winsorized sample is:

32 32 32 32 32 39 39 45 47 50 51 59 61 74 75 87 87 87 87 87

Taking the mean of the winsorized sample gives a winsorized mean; taking the variance of the winsorized sample gives a winsorized variance etc. I’ve never seen anyone using winsorized means, however the winsorized variance is used to compute the standard error of the trimmed mean (Yuen 1974). There is also a full mathematical explanation in Wilcox (2012).

You can use all the functions below to make inferences about means too, by setting tr=0. How much trimming to use is an empirical question, depending on the type of distributions you deal with. By default, all functions set tr=0.2, 20% trimming, which has been studied a lot and seems to provide a good compromise. Most functions will return an error with an alternative function suggestion if you set tr=0.5: the standard error calculation is inaccurate for the median and often the only satisfactory solution is to use a percentile bootstrap.

**Q: “With trimmed means, isn’t there a danger of users trying different amounts of trimming and reporting the one that give them significant results?”**

This is indeed a possibility, but dishonesty is a property of the user, not a property of the tool. In fact, trying different amounts of trimming could be very informative about the nature of the effects. Reporting the different results, along with graphical representations, could help provide a more detailed description of the effects.

The Yuen t-test performs better than the t-test on means in many situations. For even better results, Wilcox recommends to use trimmed means with a percentile-t bootstrap or a percentile bootstrap. With small amounts of trimming, the percentile-t bootstrap performs better; with at least 20% trimming, the percentile bootstrap is preferable. Details about these choices are available for instance in Wilcox (2012) and Wilcox & Rousselet (2017).

Yuen’s approach

1-alpha confidence interval for the trimmed mean: trimci(x,tr=.2,alpha=0.05)

Yuen t-test for 2 independent groups: yuen(x,y,tr=.2)

Yuen t-test for 2 dependent groups: yuend(x,y,tr=.2)

Bootstrap percentile-t method

One group: trimcibt(x,tr=.2,alpha=.05,nboot=599)

Two independent groups: yuenbt(x,y,tr=.2,alpha=.05,nboot=599)

Two dependent groups: ydbt(x,y,tr=.2,alpha=.05,nboot=599)

Percentile bootstrap approach

One group: trimpb(x,tr=.2,alpha=.05,nboot=2000)

Two independent groups: trimpb2(x,y,tr=.2,alpha=.05,nboot=2000)

Two dependent groups: dtrimpb(x,y=NULL,alpha=.05,con=0,est=mean)

Matlab

There are some Matlab functions here:

tm – trimmed mean

yuen – t-test for 2 independent groups

yuend – t-test for 2 dependent groups

winvar – winsorized variance

winsample – winsorized sample

wincov – winsorized covariance

These functions can be used with several estimators including  trimmed means:

pb2dg – percentile bootstrap for 2 dependent groups

pb2ig– percentile bootstrap for 2 independent groups

pbci– percentile bootstrap for 1 group

Several functions for trimming large arrays and computing confidence intervals are available in the LIMO EEG toolbox.

References

Karen K. Yuen. The two-sample trimmed t for unequal population variances, Biometrika, Volume 61, Issue 1, 1 April 1974, Pages 165–170, https://doi.org/10.1093/biomet/61.1.165

Rousselet, Guillaume; Pernet, Cyril; Wilcox, Rand (2017): Beyond differences in means: robust graphical methods to compare two groups in neuroscience. figshare. https://doi.org/10.6084/m9.figshare.4055970.v7

Rand R. Wilcox, Guillaume A. Rousselet. A guide to robust statistical methods in neuroscience bioRxiv 151811; doi: https://doi.org/10.1101/151811

Wilcox, R.R. (2012) Introduction to robust estimation and hypothesis testing. Academic Press, San Diego, CA.

Problems with small sample sizes

In psychology and neuroscience, the typical sample size is too small. I’ve recently seen several neuroscience papers with n = 3-6 animals. For instance, this article uses n = 3 mice per group in a one-way ANOVA. This is a real problem because small sample size is associated with:

  • low statistical power

  • inflated false discovery rate

  • inflated effect size estimation

  • low reproducibility

Here is a list of excellent publications covering these points:

Button, K.S., Ioannidis, J.P., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S. & Munafo, M.R. (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nature reviews. Neuroscience, 14, 365-376.

Colquhoun, D. (2014) An investigation of the false discovery rate and the misinterpretation of p-values. R Soc Open Sci, 1, 140216.

Forstmeier, W., Wagenmakers, E.J. & Parker, T.H. (2016) Detecting and avoiding likely false-positive findings – a practical guide. Biol Rev Camb Philos Soc.

Lakens, D., & Albers, C. J. (2017, September 10). When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias. Retrieved from psyarxiv.com/b7z4q

See also these two blog posts on small n:

When small samples are problematic

Low Power & Effect Sizes

Small sample size also prevents us from properly estimating and modelling the populations we sample from. As a consequence, small n stops us from answering a fundamental, yet often ignored empirical question: how do distributions differ?

This important aspect is illustrated in the figure below. Columns show distributions that differ in four different ways. The rows illustrate samples of different sizes. The scatterplots were jittered using ggforce::geom_sina in R. The vertical black bars indicate the mean of each sample. In row 1, examples 1, 3 and 4 have exactly the same mean. In example 2 the means of the two distributions differ by 2 arbitrary units. The remaining rows illustrate random subsamples of data from row 1. Above each plot, the t value, mean difference and its confidence interval are reported. Even with 100 observations we might struggle to approximate the shape of the parent population. Without additional information, it can be difficult to determine if an observation is an outlier, particularly for skewed distributions. In column 4, samples with n = 20 and n = 5 are very misleading.

figure1

Small sample size could be less of a problem in a Bayesian framework, in which information from prior experiments can be incorporated in the analyses. In the blind and significance obsessed frequentist world, small n is a recipe for disaster.