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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...
Behaviorism01:28

Behaviorism

The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
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...
Behavior Modification01:21

Behavior Modification

Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...

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Related Experiment Video

Updated: May 23, 2026

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
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Published on: November 21, 2019

Statistical inference in behavior analysis: Having my cake and eating it?

M Davison

    The Behavior Analyst
    |April 6, 2012
    PubMed
    Summary

    Simple, nonparametric statistical methods allow data to guide analysis, preventing the exclusion of unusual results. This approach helps researchers accurately identify true effects and avoid false alarms in psychological research.

    Area of Science:

    • Behavioral science
    • Psychology
    • Nonparametric statistics

    Background:

    • Traditional statistical methods can lead to the dismissal of deviant data.
    • This practice may result in overlooking valid findings or misinterpreting results.
    • Behavioral analysts and other psychologists need robust methods for data interpretation.

    Purpose of the Study:

    • To advocate for the use of simple, nonparametric statistical procedures.
    • To formalize data analysis, allowing data to "speak for themselves."
    • To prevent the arbitrary exclusion of deviant data points.

    Main Methods:

    • Application of simple, nonparametric statistical procedures.
    • Focus on data-driven analysis rather than data exclusion.

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  • Encouraging changes in publication policies.

    • Main Results:

    • Nonparametric methods can formalize data analysis.
    • These procedures help avoid the gratuitous dismissal of deviant data.
    • They serve as discriminative stimuli for identifying reliable effects.

    Conclusions:

    • Adopting nonparametric statistics enhances the accuracy of scientific findings.
    • Publication policies should be revised to support the accurate discrimination between real effects and false alarms.
    • This promotes more rigorous and reliable research in psychology and behavior analysis.