The Good Science Blog

Good science

We chose our company name, Creascience to emphasize that creativity can mix efficiently with science. In this blog, we share illustrations of how a correct use of statistics leads to drastic improvements of scientific research. Come back often to read our latest findings or simply follow this link to get notified by email (guaranteed 100% ad-free).

  • Tests for Detecting Non-Normality – Good Practices & Pitfalls

    There are several normality tests: Shapiro-Wilk, Kolmogorov-Smirnov, Anderson-Darling, Liliefors, Jarque-Bera, Anscombe-Glynn, Cramer–von Mises, etc. Pitfalls ♦️ “Non-” Normality tests” vary in the type of departures from normality they can detect and therefore it is not unlikely that they may CONTRADICT each other on the basis of their p-values.♦️ Practically speaking, you may conclude that the…

  • Show the Data, Do not Hide Them – Dynamite Plots Must Die

    Bar graphs are designed for categorical variables; yet they are commonly used to present continuous data in laboratory research, animal studies, and human studies with small sample sizes (Weissberger et al. PLOS Biology). Bar charts are ubiquitous in the life-science literature, yet a study suggests that they are often used in ways that can misrepresent…

  • One of the Top Most Cited Statistical Papers in History – 1

    Nonparametric Estimation from Incomplete Observations. Features Ideas ? The scientific article introduces the Kaplan-Meier estimator, a nonparametric method for estimating survival functions from incomplete data. ? This method is used when observations analysis are censored : when subjects drop out from the study early or never experience the event of interest by the time the…

  • The Fallacy of Placing Confidence in Confidence intervals (CIs)

    True or False❓ ☣️ If you answered “True” to any of the above statements, this is a must read paper to understand what confidence intervals truly mean. ? Morey, R.D., Hoekstra, R., Rouder, J.N. et al. The fallacy of placing confidence in confidence intervals. Psychon Bull Rev 23, 103–123 (2016). https://lnkd.in/eGZkX-uT

  • The Importance of Being Uncertain

    Statistics does not tell us whether we are right. It tells us the chances of being wrong. Krzywinski, M., Altman, N. Importance of being uncertain. Nat Methods 10, 809–810 (2013). https://doi.org/10.1038/nmeth.2613 Understanding Replication & Uncertainty When an experiment is reproduced, we almost never obtain exactly the same results. Instead, repeated measurements span a range of values because…