Better Ways to Improve Standards in Brain-Behavior Correlation Analysis

Rousselet and Pernet (2012) demonstrate that outliers can skew Pearson correlation. They claim that this leads to widespread statistical errors by selecting and re-analyzing a cohort of published studies. However, they report neither the study identities nor inclusion criteria for this survey, so their claim cannot be independently replicated. Moreover, because their selection criteria are based on the authors’ belief that a study used misleading statistics, their study represents an example of “double dipping” (Kriegeskorte et al., 2009). The strong claims they make about the literature are therefore circular and unjustified by their data. Their purely statistical approach also does not consider the biological context of what observations constitute outliers. In discussion, they propose that the skipped correlation (Wilcox, 2005) is an appropriate alternative to the Pearson correlation that is robust to outliers. However, this test lacks statistical power to detect true relationships (Figure ​(Figure1A)1A) and is highly prone to false positives (Figure ​(Figure1B). 1B). These factors conspire to drastically reduce the sensitivity of this test in comparison to other procedures (Appendix 1). Further, it is susceptible to the parameters chosen for the minimum covariance estimator to identify outliers but these parameters are not reported. Figure 1 Statistical power (A) and false positive rates (B) for four statistical tests and four sample sizes based on 10,000 simulations (see Appendix 1 for details). Outliers can drastically inflate false positives for Pearson correlation (note the difference . Their argument fails to consider a broad literature on robust statistics, although an extensive review is outside the scope of this commentary. We limit ourselves instead to presenting a practical alternative to their approach: Shepherd’s pi correlation (http://www.fil.ion.ucl.ac.uk/~sschwarz/Shepherd.zip). We identify outliers by bootstrapping the Mahalanobis distance, Ds, of each observation from the bivariate mean and excluding all points whose average Ds is 6 or greater. Shepherd’s pi is Spearman’s rho but the p-statistic is doubled to account for outlier removal (Appendix 2). This compares very well in power (Figure ​(Figure1A)1A) to other tests and is more robust to the presence of influential outliers (Figure ​(Figure1B). 1B). We replot the data Rousselet and Pernet presented in their Figure 2. The conclusions drawn from Shepherd’s pi are comparable to skipped correlation but less strict in situations where a relationship is likely (Figure ​(Figure1C,1C, Figures ​FiguresA1A1 and ​andA2A2 in Appendix). Consider for instance the data in Figure ​Figure1C-1.1C-1. Pearson and Spearman correlation applied to these data are comparable. This implies that the assumptions of Pearson’s r were probably met in this case. The skipped correlation (r’) does not reach significance but nevertheless shows a similar relationship, consistent with our demonstration above that it is too conservative a measure. Under Shepherd’s pi, however, the relationship between these variables is significant. Indeed, reflecting our intimate knowledge of these data (Schwarzkopf et al., 2011), we already know that the relationship studied here replicates for separate behavioral measures (see Schwarzkopf et al., 2011 SOM). A similar pattern was observed for other data, e.g., Figure ​Figure1C-2.1C-2. In some cases skipped correlation even removes the majority of data as outliers (e.g., their Figure 2E), which borders on the absurd. Rousselet and Pernet also claim that none of the studies that they surveyed considered the correlation coefficient and its confidence intervals. Cohen defined that 0.3 0.5, that is, at least 25% of the variance is explained. A correlation accounting for ~15% of variance is thus not particularly “modest” as they state. Naturally, this taxonomy is somewhat arbitrary but when relating complex cognitive functions to brain measures we are unlikely to find very high r, except for trivial relationships (Yarkoni, 2009). Their failure to find reported confidence intervals in the literature is also puzzling because it does not accurately report the published work they considered. For example, our study, reproduced in their Figure 2A, reported bootstrapped 95% confidence intervals in the figure (Schwarzkopf et al., 2011). They also do not consider important aspects of what confidence intervals reflect. Naturally, a confidence interval is an indicator of the certainty with which the effect size can be estimated. However, it depends on three factors: the strength of the correlation, the sample size, and the data distribution. Because Pearson correlation assumes a Gaussian distribution we can predict the confidence interval for any given r. If the bootstrapped confidence interval differs from this prediction, the data probably do not meet the assumptions. Rousselet and Pernet’s example for bivariate outliers (their Figure 1D) illustrates this: the predicted confidence interval for r = 0.49 with n = 17 should be (0.01, 0.79). However, the bootstrapped confidence interval for this example is (−0.19, 0.87), much wider and also overlapping zero. This indicates that outliers skew the correlation and that it should not be considered significant. Compare this to Figure ​Figure1C-11C-1 (their Figure 2A): the nominal confidence interval should be (−0.65, −0.02); the actual bootstrapped interval is very similar: (−0.67, −0.03). Therefore, the use of Pearson/Spearman correlation was justified here. We propose simple guidelines to follow when testing correlations. First, use Spearman’s rho because it captures non-linear relationships. Second, bootstrap confidence intervals. Third, if the interval differs from the nominal interval, apply Shepherd’s pi as a more robust test. Fourth, estimate the reliability of individual observations, especially in cases where outliers strongly affect results. Outliers are frequently the result of artifacts or measurement error. Our last point highlights an important general concern we have with Rousselet and Pernet’s argument. Statistical tests are important tools to be used by researchers for interpreting their data. However, the goal of neuroscience is to answer biologically relevant questions, not to produce statistically significant results. No statistical procedure can determine whether a biological question is valid or if a theory is sound. Rather, one has to inspect each finding and each data point in its own right, evaluating the data quality and the potential confounds on a case-by-case basis. Outliers should not be determined solely by statistical tests but must take into account biological interpretation (Bertolino, 2011; Schott and Duzel, 2011). And finally, there is only one way any finding can be considered truly significant; when upon repeated replication it passes the test of time.