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Large standard deviations and logarithmic-normality

Richard Paul Sorrentino

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Abstract

While many quantifiable biological phenomena can be described by making use of an assumption of normality in the distribution of individual values, many biological phenomena are not accurately described by the normal distribution. An unquestioned assumption of normality of distribution of possible outcomes can lead to misinterpretation of data, which could have serious consequences. Thus it is extremely important to test the validity of an assumption of normality of possible outcomes. As it turns out, the logarithmic-normal (log-normal) distribution pattern is often far more accurate in describing statistical biological phenomena. Herein I examine large samples of values for circulating blood cell (hemocyte) concentration (CHC) among both wild-type and mutant Drosophila larvae, and demonstrate in both cases that the distribution of individual values does not conform to normality, but does conform to log-normality.

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What this paper is about

While many quantifiable biological phenomena can be described by making use of an assumption of normality in the distribution of individual values, many biological phenomena are not accurately described by the normal distribution. An unquestioned assumption of normality of distribution of possible outcomes can lead to misinterpretation of data, which could have serious consequences. Thus it is extremely important to test the validity of an assumption of normality of possible outcomes. As it turns out, the logarithmic-normal (log-normal) distribution pattern is often far more accurate in describing statistical biological phenomena. Herein I examine large samples of values for circulating blood cell (hemocyte) concentration (CHC) among both wild-type and mutant Drosophila larvae, and demonstrate in both cases that the distribution of individual values does not conform to normality, but does conform to log-normality.

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Available abstract

While many quantifiable biological phenomena can be described by making use of an assumption of normality in the distribution of individual values, many biological phenomena are not accurately described by the normal distribution. An unquestioned assumption of normality of distribution of possible outcomes can lead to misinterpretation of data, which could have serious consequences. Thus it is extremely important to test the validity of an assumption of normality of possible outcomes. As it turns out, the logarithmic-normal (log-normal) distribution pattern is often far more accurate in describing statistical biological phenomena. Herein I examine large samples of values for circulating blood cell (hemocyte) concentration (CHC) among both wild-type and mutant Drosophila larvae, and demonstrate in both cases that the distribution of individual values does not conform to normality, but does conform to log-normality.

Key concepts: Normality, Normal distribution, Logarithm, Statistics, Distribution (mathematics), Econometrics, Standard deviation, Normality test

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