Distribution of data in cellular electrophysiology: Is it always normal?
Jazyk angličtina Země Velká Británie, Anglie Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
32621819
DOI
10.1016/j.pbiomolbio.2020.05.008
PII: S0079-6107(20)30041-9
Knihovny.cz E-zdroje
- Klíčová slova
- Cardiomyocyte, Gamma distribution, Geometric mean, Inward rectifier, Log-normal distribution, Median, Membrane capacitance, Normal distribution,
- MeSH
- algoritmy MeSH
- buněčná membrána patologie fyziologie MeSH
- elektrická kapacitance MeSH
- elektrody MeSH
- elektrofyziologie metody MeSH
- interpretace statistických dat MeSH
- krysa rodu Rattus MeSH
- membránové potenciály MeSH
- normální rozdělení * MeSH
- potkani Wistar MeSH
- reprodukovatelnost výsledků MeSH
- srdeční síně patologie MeSH
- svalové buňky fyziologie MeSH
- teoretické modely MeSH
- zvířata MeSH
- Check Tag
- krysa rodu Rattus MeSH
- mužské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The distribution of data presented in many electrophysiological studies is presumed to be normal without any convincing evidence. To test this presumption, the cell membrane capacitance and magnitude of inward rectifier potassium currents were recorded by the whole-cell patch clamp technique in rat atrial myocytes. Statistical analysis of the data showed that these variables were not distributed normally. Instead, a positively skewed distribution appeared to be a better approximation of the real data distribution. Consequently, the arithmetic mean, used inappropriately in such data, may substantially overestimate the true mean value characterizing the central tendency of the data. Moreover, a large standard deviation describing the variance of positively skewed data allowed 95% confidence interval to include unrealistic negative values. We therefore conclude that the normality of the electrophysiological data should be tested in every experiment and, if rejected, the positively skewed data should be more accurately characterized by the median and interpercentile range or, if justified (namely in the case of log-normal and gamma data distribution), by the geometric mean and the geometric standard deviation.
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