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    Area of Science:

    • Behavior Analysis
    • Psychology
    • Research Methodology

    Background:

    • Single-subject designs are a cornerstone of behavior analysis.
    • Statistical inference is widely used across scientific disciplines.
    • A perceived gap exists in the methodological sophistication of behavior analysts regarding statistical inference.

    Purpose of the Study:

    • To highlight the fundamental similarities between single-subject and statistical inference methods.
    • To advocate for the integration of statistical inferential procedures within behavior analysis.
    • To underscore the benefits of statistical knowledge for behavior analysts in research and publication.

    Main Methods:

    • Comparative analysis of core inferential principles in single-subject designs and statistical inference.
    • Identification of shared concepts such as variability, replication, effect size, internal validity, and generalizability.
    • Discussion of the practical implications for behavior analysts.

    Main Results:

    • Single-subject and statistical inference share virtually identical principles for inferring change.
    • Key parallels include the role of variability, replication, effect size, internal validity, and generalizability.
    • Statistical inferential procedures offer a complementary approach to single-subject methods.

    Conclusions:

    • Behavior analysts would benefit from adopting statistical inferential procedures to enhance methodological rigor.
    • Proficiency in statistical inference can improve critical evaluation of research, grant acquisition, and publication diversity.
    • Bridging the gap between single-subject and statistical inference can increase the visibility and impact of behavior-analytic research.