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Related Concept Videos

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...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
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Testing 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...
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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...
Reliability and Validity01:29

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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...

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Reliability of PET activation across statistical methods, subject groups, and sample sizes.

T J Grabowski1, R J Frank, C K Brown

  • 1Department of Neurology, Division of Behavioral Neurology and Cognitive Neuroscience, University of Iowa, Iowa City, Iowa 52242, USA.

Human Brain Mapping
|April 22, 2010
PubMed
Summary
This summary is machine-generated.

Comparing four pixel-based positron emission tomography (PET) analysis methods, researchers found that while all methods controlled false positives, those using pooled variance showed better stability. Further improvements in anatomical standardization could enhance power.

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Medical Imaging Analysis

Background:

  • Positron emission tomography (PET) is crucial for visualizing brain activity.
  • Accurate estimation of regional activation is vital for interpreting PET studies.
  • Various pixel-based statistical methods exist for PET activation analysis, each with potential strengths and weaknesses.

Purpose of the Study:

  • To compare the performance of four distinct pixel-based methods for estimating regional activation in PET images.
  • To evaluate the impact of technical factors on method performance under controlled conditions.
  • To assess the replicability and reliability of activation detection across different methods.

Main Methods:

  • Implementation and comparison of four pixel-based methods: change distribution analysis, Worsley's method, a pixelwise general linear model, and a nonparametric method.
  • Utilized a large dataset from a verb generation paradigm with 18 normal subjects.
  • Held key technical factors constant, including smoothing, stereotactic transform, coregistration, search volume, and alpha level.

Main Results:

  • All methods controlled Type I errors (false positives) at the nominal rate in noise datasets.
  • Detected regions of activation demonstrated high internal replication (93%).
  • Methods differed primarily in Type II error rates and the stability of activation cluster locations; local variance-based methods were less powerful and stable with small sample sizes.

Conclusions:

  • Worsley's method using pooled variance improved stability but faced challenges with non-stationary variance.
  • The power of current pixel-based analyses is modest with conventional sample sizes.
  • Modeling variance sources and improving anatomical standardization are key to enhancing the power of PET activation analyses.