Related Experiment Video
Updated: Jun 12, 2026
08:59
A Cost Effective and Adaptable Scratch Migration Assay
Published on: June 30, 2020
A model-averaging approach to replication: the case of p rep
Geoffrey J Iverson1, Eric-Jan Wagenmakers, Michael D Lee
1Department of Cognitive Science, University of California, CA, USA.
Psychological Methods
|June 3, 2010
Summary
The prep statistic overestimates the probability of concurrence, potentially by over 30%. This method may lead researchers to overestimate the reliability of their experimental findings.
Area of Science:
- Psychological Science
- Statistics
- Research Methodology
Background:
- The prep statistic is proposed to estimate the probability of experimental concurrence.
- Psychological Science endorses prep over traditional methods for estimating concurrence.
- Prep assumes all effect sizes are equally likely a priori, neglecting the null hypothesis.
Purpose of the Study:
- To demonstrate that the prep statistic overestimates the probability of concurrence.
- To investigate the impact of the null hypothesis on concurrence probability.
- To highlight the potential for prep to inflate perceived experimental reliability.
Main Methods:
- Theoretical derivation of concurrence probability under different prior assumptions.
- Analysis of the influence of the null hypothesis posterior probability on concurrence.
- Comparison of prep estimates with scenarios incorporating a null hypothesis.
Main Results:
- Prep overestimates the probability of concurrence when a null hypothesis is considered.
- The posterior probability of the null hypothesis significantly affects concurrence estimates.
- Overestimation by prep can exceed 30% when priors for null and alternative hypotheses are equal.
Conclusions:
- Prep provides an upper bound for the probability of concurrence.
- Overreliance on prep may lead to an overestimation of experimental effect reliability.
- Researchers should be cautious about interpreting prep estimates due to potential overestimation.
Related Concept Videos
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:24Testing a Claim about Population Proportion
A complete procedure for testing a claim about a population proportion is provided here.
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...
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...
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
01:11Statistical Analysis: Overview
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
01:12Sampling Distribution
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
02:13The Representativeness Heuristic
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.