A solid understanding of inferential statistics is of major importance for designing and interpreting empirical results in any<br>scientific discipline. However, students are prone to many misconceptions regarding this topic. This article structurally summarizes<br>and describes these misconceptions by presenting a systematic review of publications that provide empirical evidence of them. This<br>group of publications was found to be dispersed over a wide range of specialized journals and proceedings, and the methodology<br>used in the empirical studies was very diverse. Three research needs rise from this review: (1) further empirical studies that identify<br>the sources and possible solutions for misconceptions in order to complement the abundant theoretical and statistical discussion<br>about them; (2) new insights into effective research designs and methodologies to perform this type of research; and (3) structured<br>and systematic summaries of findings like the one presented here, concerning misconceptions in other areas of statistics, that might<br>be of interest both for educational researchers and teachers of statistics.<br>© 2007 Elsevier Ltd. All rights reserved.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education