相关实验视频

Updated: May 21, 2025

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
05:57

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

Published on: November 20, 2018

55.2K

在估计行为测试-重新测试可靠性时,样本大小很重要.

Brendan Williams1,2, Lily FitzGibbon3, Daniel Brady4,5

  • 1Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Harry Pitt Building, Reading, UK. b.williams3@reading.ac.uk.

Behavior research methods
|March 22, 2025
PubMed
概括

类内相关系数 (ICC) 可靠地测量反转学习,但准确的差异成分估计需要比通常使用的更大的样本大小. 差异分解对于强大的可靠性研究至关重要.

关键词:
认知灵活性 认知灵活性计算建模计算建模强化学习是一种强化学习.可靠性 可靠性可靠性逆向学习是一种反向学习.样本的大小 样本大小测试重复测试试验

更多相关视频

A Within-Subject Experimental Design using an Object Location Task in Rats09:28

A Within-Subject Experimental Design using an Object Location Task in Rats

Published on: May 6, 2021

4.4K
A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice07:18

A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice

Published on: March 2, 2019

19.0K

相关概念视频

Sample Size Calculation01:19

Sample Size Calculation

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...
3.2K
Statistical Significance01:50

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...
20.1K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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...
3.1K
Contaminants and Errors01:16

Contaminants and Errors

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...
81
Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
3.3K
Central Limit Theorem01:14

Central Limit Theorem

The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
14.3K