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When comparing two independent groups, psychology researchers commonly use Student's t-tests. Assumptions of normality and homogeneity of variance underlie this test. More often than not, when these conditions are not met, Student's t-test can be severely biased and lead to invalid statistical inferences. Moreover, we argue that the assumption of equal variances will seldom hold in psychological research, and choosing between Student's t-test and Welch's t-test based on the outcomes of a test of the equality of variances often fails to provide an appropriate answer. We show that the Welch's t-test provides a better control of Type 1 error rates when the assumption of homogeneity of variance is not met, and it loses little robustness compared to Student's t-test when the assumptions are met. We argue that Welch's t-test should be used as a default strategy. Keywords:Welch's t-test; Student's t-test; homogeneity of variance; Levene's test; Homoscedasticity; statistical power; type 1 error; type 2 error Independent sample t-tests are commonly used in the psychological literature to statistically test differences between means. There are different types of t-tests, such as Student's t-test, Welch's t-test, Yuen's t-test, and a bootstrapped t-test. These variations differ in the underlying assumptions about whether data is normally distributed and whether variances in both groups are equal (see, e.g., Rasch, Kubinger, & Moder, 2011;Yuen, 1974). Student's t-test is the default method to compare two groups in psychology. The alternatives that are available are considerably less often reported. This is surprising, since Welch's t-test is often the preferred choice and is available in practically all statistical software packages.In this article, we will review the differences between Welch's t-test, Student's t-test, and Yuen's t-test, and we suggest that Welch's t-test is a better default for the social sciences than Student's and Yuen's t-tests. We do not include the bootstrapped t-test because it is known to fail in specific situations, such as when there are unequal sample sizes and standard deviations differ moderately (Hayes & Cai, 2007).When performing a t-test, several software packages (i.e., R and Minitab) present Welch's t-test by default. Users can request Student's t-test, but only after explicitly stating that the assumption of equal variances is met. Student's t-test is a parametric test, which means it relies on assumptions about the data that are analyzed. Parametric tests are believed to be more powerful than non-parametric tests (i.e., tests that do not require assumptions about the population parameters; Sheskin, 2003). However, Student's t-test is generally only more powerful when the data are normally distributed (the assumption of normality) and the variances are equal in both groups (homoscedasticity; the assumption of homogeneity of variance; Carroll & Schneider, 1985;Erceg-Hurn & Mirosevich, 2008).When sample sizes are equal between groups, Student's t-test is robust to violations of the assump...

Emotional intelligence (EI) can be defined as the ability to identify, express, understand, manage, and use emotions. EI has been shown to have an important impact on health, relationships, and work/academic performance. In this article, we present a systematic review of 46 EI intervention studies on adult populations in order to assess their outcomes. Overall, these findings provide some support for the efficacy of EI programs. However, important limitations in most of the studies restrict the generalizability of their results. We discuss the contributions and limitations of these studies and make recommendations for the development and implementation of future interventions.

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Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even more frequently, researchers do not pre-specify how they plan to manage outliers. In this paper we aim to improve research practices by outlining what you need to know about outliers. We start by providing a functional definition of outliers. We then lay down an appropriate nomenclature/classification of outliers. This nomenclature is used to understand what kinds of outliers can be encountered and serves as a guideline to make appropriate decisions regarding the conservation, deletion, or recoding of outliers. These decisions might impact the validity of statistical inferences as well as the reproducibility of our experiments. To be able to make informed decisions about outliers you first need proper detection tools. We remind readers why the most common outlier detection methods are problematic and recommend the use of the median absolute deviation to detect univariate outliers, and of the Mahalanobis-MCD distance to detect multivariate outliers. An R package was created that can be used to easily perform these detection tests. Finally, we promote the use of pre-registration to avoid flexibility in data analysis when handling outliers.

When comparing independent groups researchers often analyze the means by performing a Student's t-test or classical Analysis of Variance (ANOVA) F-test (Erceg-Hurn & Mirosevich, 2008; Keselman et al., 1998; Tomarken & Serlin, 1986). Both tests rely on the assumptions that independent and identically distributed residuals (1) are sampled from a normal distribution and (2) have equal variances between groups (or homoscedasticity; see Lix, Keselman, & Keselman, 1996). While a deviation from the normality assumption generally does not strongly affect either the Type I error rates (Glass,

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ObjectiveEvaluate safety and effectiveness of VYC-12 (Juvéderm Volite; an injectable crosslinked hyaluronic acid gel designed to improve skin quality attributes such as surface smoothness and hydration) for facial intradermal injection.Materials and methodsIn a prospective, single-arm study, subjects with moderate/severe cheek skin roughness per Allergan Skin Roughness Scale (ASRS) received VYC-12 in the cheeks and forehead, and/or neck, with touch-up treatment to correct asymmetry 30 days later and optional repeat treatment 9 months after last treatment. The primary effectiveness measure was ASRS responder rate (percentage of cheeks with ≥1-point improvement from baseline) at month 1. Skin hydration was instrument-assessed.ResultsOf 131 subjects treated, 31 (23.7%) received touch-up treatment. ASRS responder rate was 96.2% at month 1, 76.3% at month 4, 34.9% at month 6, and 87.1% after repeat treatment. Responder rate in cheeks with severe baseline roughness was 93.8%, 83.1%, and 52.3% at months 1, 4, and 6, respectively. Skin hydration improved significantly (P<0.01) from baseline at all timepoints through month 9. Injection site responses were as expected. All treatment-related adverse events were mild/moderate.ConclusionVYC-12 safely and effectively improved skin smoothness up to 6 months and hydration lasting 9 months.