Once upon a time, when you were in graduate school, you were told that before applying statistic parametric tests, such as t-test and ANOVA, you should make sure that your data complied with normality and homoscedasticity. Surely, you were told. Then, why does this basic statistical knowledge seems evaporated from many current Neurobiology papers? As a journal reviewer, I find that, most often than not, research paper authors skip assessing normality and homoscedasticity and go straight into applying t-tests and ANOVA. In this essay, I will explain in simple terms the meaning of these two concepts, normality and homoscedasticity, and their impact on the power of parametric tests. Of course, analogies and simple explanations do not convey the full complexity of Statistics. ACKNOWLEDGEMENTSI would like to dedicate this Commentary to the memory of my dear Agora Highschool Math teacher, Eugenio Rodrigo, who taught me to love Mathematics. I am deeply grateful for enriching discussions to Eva Benito, Luis Miguel García-Segura, Carlos Matute, students of the Achucarro Introductory course on Statistics for Neurobiologists, and researchers of the Sierra lab, who inspired this article. This work was supported by grants from the Spanish Ministry of Science and Innovation (https://www.ciencia.gob.es/) with FEDER funds to A.S. (RTI2018-099267-B-I00) and a Tatiana Foundation project (P-048-FTPGB 2018).