Power to Detect What? Considerations for Planning and Evaluating Sample Size
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Researchers in social-personality psychology are re-evaluating sample size and power analysis due to the replication crisis. This work proposes cost-effective sample size determination and discusses alternatives to power analysis for robust research practices.
Area Of Science
- Psychology
- Social Psychology
- Personality Psychology
Background
- The replication crisis has prompted increased scrutiny of sample size adequacy in social and personality psychology.
- Concerns exist regarding studies with insufficient participants potentially leading to unreliable conclusions.
Purpose Of The Study
- To analyze current controversies surrounding power analysis and sample size adequacy.
- To propose alternative methods for determining sample sizes, especially for novel research questions.
- To offer recommendations for researchers, reviewers, and editors to improve research practices.
Main Methods
- Analysis of current controversies in power analysis and sample size determination.
- Discussion of effect size selection, application of power analyses to existing data, and mitigation strategies for specific research types.
- Exploration of precision analysis and sequential analysis as alternatives to traditional power analysis.
Main Results
- Advocates for basing sample sizes on cost-effective effects for novel research, considering practical implementation and study costs.
- Highlights the need to address how power analysis requirements impact research with hard-to-reach and marginalized populations.
- Identifies key areas of controversy including effect size estimation and the utility of power analysis on already-collected data.
Conclusions
- Recommends a shift towards cost-effectiveness in sample size determination for novel research.
- Suggests precision and sequential analyses as viable alternatives to power analysis.
- Calls for improved practices among researchers, reviewers, and editors to enhance the rigor of social-personality psychology research.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
In the equation, n is the sample size, ͞x is the sample mean, x̿ is the combined mean for all the observations, k is the number of samples, and s2 is the variance of the sample. It should be noted that the subscript 'i'...
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
The decision is not to reject null hypothesis when it is true (correct decision).
The decision is to reject the null hypothesis when it is true (incorrect decision known as a Type I error).
The decision is not to reject the null hypothesis when, in fact, it is false (incorrect decision known as a Type II error).
The...