The Case Against Statistical Significance Testing, Revisited
Ronald P. Carver
Abstract
Ronald P. Carver
Abstract
At present, too many research results in education are blatantly described as significant, when they are in fact trivially small and unimportant. There are several things researchers can do to minimize the importance of statistical significance testing and get articles published without using these tests. First, they can insert statistically in front of significant in research reports. Second, results can be interpreted before p values are reported. Third, effect sizes can be reported along with measures of sampling error. Fourth, replication can be built into the design. The touting of insignificant results as significant because they are statistically significant is not likely to change until researchers break the stranglehold that statistical significance testing has on journal editors.
OpenAlex reports 325 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
At present, too many research results in education are blatantly described as significant, when they are in fact trivially small and unimportant. There are several things researchers can do to minimize the importance of statistical significance testing and get articles published without using these tests. First, they can insert statistically in front of significant in research reports. Second, results can be interpreted before p values are reported. Third, effect sizes can be reported along with measures of sampling error. Fourth, replication can be built into the design. The touting of insignificant results as significant because they are statistically significant is not likely to change until researchers break the stranglehold that statistical significance testing has on journal editors.
Key concepts: Statistical significance, Replication (statistics), Statistical hypothesis testing, Statistics, Statistical analysis, Statistical evidence, Sampling (signal processing), Test (biology)