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Abstract <p>In some branches of physiology (for example, space physiology), researchers have to deal with small samples, which makes it impossible to use classical data analysis methods and requires different approaches. Small samples are characterized by an increased influence of individual characteristics of a particular organism on the nature of the adaptation process, and therefore, an urgent task is to separate the effect of the influencing factor and individual reactions. The authors propose a new approach to the analysis of these small samples on the example of adaptive changes in the cardiovascular system (CVS) in women when reproducing the effects of microgravity in the conditions of 5-day “dry” immersion (DI). Changes in the CVS were assessed by indicators reflecting hemodynamics and autonomic modulating effects on heart rhythm. The aim of the work was to identify indicators reflecting the effect of the influencing factor, as well as indicators reflecting the individual characteristics of the subjects of the experimental sample. Methodological approach to data analysis based on analysis of variance (ANOVA) was used for this purpose. Full analysis of the small sample data with a statistically justified separation of the influence of both the studied factor and individual reactions was carried out, and specific subjects that affect the uniformity of the entire sample were identified. The presented approach allows selecting those indicators that reflect the impact of the studied factor at the initial stage of analysis, and, accordingly, meet the set goals, while excluding indicators in which the contribution of individual characteristics is so large that it makes them inappropriate for consideration in the current scientific search.</p>

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Application of the Variance Analysis in Small Sample Statistics in Physiological Research

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • M. V. Fedchuk,
  • V. B. Rusanov,
  • A. M. Nosovsky,
  • O. I. Orlov

摘要

Abstract

In some branches of physiology (for example, space physiology), researchers have to deal with small samples, which makes it impossible to use classical data analysis methods and requires different approaches. Small samples are characterized by an increased influence of individual characteristics of a particular organism on the nature of the adaptation process, and therefore, an urgent task is to separate the effect of the influencing factor and individual reactions. The authors propose a new approach to the analysis of these small samples on the example of adaptive changes in the cardiovascular system (CVS) in women when reproducing the effects of microgravity in the conditions of 5-day “dry” immersion (DI). Changes in the CVS were assessed by indicators reflecting hemodynamics and autonomic modulating effects on heart rhythm. The aim of the work was to identify indicators reflecting the effect of the influencing factor, as well as indicators reflecting the individual characteristics of the subjects of the experimental sample. Methodological approach to data analysis based on analysis of variance (ANOVA) was used for this purpose. Full analysis of the small sample data with a statistically justified separation of the influence of both the studied factor and individual reactions was carried out, and specific subjects that affect the uniformity of the entire sample were identified. The presented approach allows selecting those indicators that reflect the impact of the studied factor at the initial stage of analysis, and, accordingly, meet the set goals, while excluding indicators in which the contribution of individual characteristics is so large that it makes them inappropriate for consideration in the current scientific search.