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Related Concept Videos

Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
Statistical Significance01:37

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
Confirmation Biases01:31

Confirmation Biases

The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
What is an Experiment?01:12

What is an Experiment?

An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...

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Related Experiment Video

Updated: Jul 9, 2026

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
07:05

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Refutation Text Facilitates Learning: a Meta-Analysis of Between-Subjects Experiments.

Noah L Schroeder1, Aurelia C Kucera2

  • 1Department of Leadership Studies in Education and Organizations, Wright State University, 3640 Colonel Glenn Highway, Dayton, OH 45435 USA.

Educational Psychology Review
|January 31, 2022
PubMed
Summary

Refutation texts effectively correct scientific misconceptions, improving learning across diverse subjects. This instructional method offers a cost-effective way to enhance scientific understanding for the public.

Keywords:
Conceptual changeLearningMeta-analysisRefutationRefutation text

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Area of Science:

  • Educational Psychology
  • Science Communication
  • Cognitive Science

Background:

  • Scientific misconceptions are widespread and can hinder public health and scientific literacy.
  • Refutation texts are a low-cost instructional strategy to address these misconceptions.

Purpose of the Study:

  • To evaluate the effectiveness of refutation text structure on learning outcomes.
  • To determine the impact of refutation texts on correcting scientific misconceptions.

Main Methods:

  • A random-effects meta-analysis was performed.
  • Data from 44 independent comparisons (n=3,869) were analyzed.
  • Effectiveness was compared against other learning conditions.

Main Results:

  • Refutation text demonstrated a positive, moderate effect size (g=0.41, p<.001) on learning.
  • The positive effect was consistent and robust across various educational contexts.

Conclusions:

  • Refutation texts are a valuable tool for improving scientific understanding.
  • Implementation of refutation texts can facilitate learning in numerous scientific fields.