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On the Application of Statistical Analysis for Interpretation of Experimental Results in Environmental Microbiology

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Abstract—

Statistical analysis is an integral part of every experiment, since it helps researchers to make conclusions out of the work done. Textbooks with detailed description of the algorithms for statistical analysis exist, as well as diverse software packages. Statistical methods are, however, often applied incorrectly, which leads to erroneous and inadequate conclusions. The present article is an attempt to determine the main problems emerging in the course of statistical analysis of experimental results in the field of environmental microbiology. For instance, the classical parametric tests, which are most often used in experimental articles (t-test, analysis of variance, Pearson correlation coefficient, etc.), are applicable only when a random variable of n independent observations has normal distribution. Importantly, the normality of the distribution must be proved, and it is possible to do only for minimal sample size of 20‒30 independent observations per group. The latter is crucial for the incubation, isotope, and molecular biological experiments. The family of normal distributions is not the only family of parameter-dependent (parametric) distributions of random variables. Moreover, real-life distributions usually differ from the normal ones, and normal distribution may be considered only as a certain approximation. High diversity of parametric families complicates the choice of criteria and statistical tests for analysis of a set of data collected from n independent observations. Nonparametric statistics may be of help, since it is free of sample size and distribution requirements, although, as any method of analysis, it also has limitations. An increasing number of experimental data, including those in the field of environmental microbiology, are nowadays analyzed by using nonparametric statistics, which indicates a certain tendency for substitution of parametric methods by nonparametric ones.

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Notes

  1. Parametric statistics deals with sample data that follows a probability distribution described by analytical formula with a small number of parameters (these distributions are termed parametric). Distribution (of probabilities) is a function determining the probability that a random variable accepts a given value or falls within a given interval. In parametric estimation tasks, a probabilistic model is accepted, according to which observations x1, x2,…, xn are interpreted as realization of n independent random variables with the distribution function F(x; θ). Here θ is an unknown vector parameter (of a fixed final dimension, including dimension 1, when θ is a single number) in the parameter space Θ, which is assigned by the probabilistic model used. The goal of estimation is to determine the point estimate and confidence intervals (or confidence range) for the components of the θ vector parameter. Nonparametric statistical methods are not based on the suggestion of the distribution functions belonging to a given parametric family, i.e., they do not require the distribution function to be given as a specific analytical formula (Orlov, 2004).

  2. A sample is a number of independent similarly distributed random elements, i.e., a set of observed values (x1, x2, …, xn) or a set of objects taken from a studied totality, where n (sample size) is the number of observed values in the sample (Orlov, 2004).

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ACKNOWLEDGMENTS

The work was supported by the Russian Science Foundation, project no. 16-14-10201.

Author information

Authors and Affiliations

  1. Winogradsky Institute of Microbiology, Research Center of Biotechnology, Russian Academy of Sciences, 119071, Moscow, Russia

    A. Yu. Kallistova & N. V. Pimenov

  2. Yugra State University, 628011, Khanty-Mansiisk, Russia

    A. F. Sabrekov & M. V. Glagolev

  3. Tomsk State University, 634050, Tomsk, Russia

    A. F. Sabrekov & M. V. Glagolev

  4. Moscow State University, 119991, Moscow, Russia

    V. M. Goncharov & M. V. Glagolev

  5. Institute of Forest Science, Russian Academy of Sciences, 143030, Uspenskoe, Moscow oblast, Russia

    M. V. Glagolev

Authors
  1. A. Yu. Kallistova
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  2. A. F. Sabrekov
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  3. V. M. Goncharov
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  4. N. V. Pimenov
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  5. M. V. Glagolev
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Correspondence to A. Yu. Kallistova.

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The authors declare that they have no conflict of interest. This article does not contain any studies involving animals or human participants performed by any of the authors.

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Kallistova, A.Y., Sabrekov, A.F., Goncharov, V.M. et al. On the Application of Statistical Analysis for Interpretation of Experimental Results in Environmental Microbiology. Microbiology 88, 232–239 (2019). https://doi.org/10.1134/S002626171902005X

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  • DOI: https://doi.org/10.1134/S002626171902005X

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