Related Experiment Video
Updated: May 25, 2026
Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
Published on: November 20, 2018
An extended refutation text and funny videos reduce notorious p-value misconceptions
1Department of Psychology, Bielefeld University, 33615, Bielefeld, Germany. markus.hefter@uni-bielefeld.de.
None:
For many decades, the vast majority of psychology students and their supervisors alike have been prone to misunderstanding the p-value, such as confusing it with a hypothesis' probability. The present paper offers three innovative contributions to address this problem: (1) A novel yet valid methodological approach to assess p-value misconceptions that combines correctness and subjective certainty. (2) An effective short-term digital intervention built around an extended refutation text to reduce those misconceptions in about 10 min. (3) An unorthodox humor intervention with funny videos for extra effectiveness (and chuckles). A sample of 157 undergraduate psychology students (Mage = 23.36) took part in an online experiment featuring a 2×2-factorial design (funny vs. neutral videos and intervention vs. control condition. Planned contrast analyses revealed a large positive effect of the digital intervention on the participants' conceptual change score. Furthermore, the prior presentation of funny videos did indeed yield an additional positive effect. Overall, the findings demonstrate the potential of extended refutation texts (enhanced with funny videos) for a short and convenient digital refresher aimed at psychology students who had already taken an introductory course but needed further clarification to tackle p-value misconceptions.
Related Concept Videos
01:09Decision Making: P-value Method
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
01:10P-value
P-value stands for the probability value. P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
01:24Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
01:37Statistical Significance
01:17Hypothesis: Accept or Fail to Reject?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...
01:14Decision Making: Traditional Method
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...