***** FRIDAY (FUN)1 FACT ******

1 Fun to follow in coming weeks.

 

It’s a common question that gets asked: What measure should I use in my error bars? If you also struggle with that, here’s a recap of two of the most common options, and when to use them.

 

Standard deviation (SD) is a characteristic of a distribution – it is specific to the observations that you have in the data, and is used to describe or summarise their qualities. It tells you how far your observations are from the mean value. There is a population standard deviation (σ), and a sample standard deviation (s), where the former assumes you know all the observations there can be, and the other assumes you have a sample of them – both are still measures of dispersion in the observations. If you are running experiments, you quite certainly deal with the latter.

 

Standard error (of the mean) (SE or SEM) is a characteristic of an estimate. It is the variation of the sample means, and tells you how far the “true mean” of the trait is likely be from the one you measured, and within what range you would expect the mean to be if you repeated the sampling. SE tells you what is the potential error in the results.

 

 

Curiosity: Danish Statistics text books do not have a Danish term for standard error. Sometimes “spredning” is used, which refers to all dispersion parameters (SD, variance, SE, etc.), and as such is imprecise. When specifically detailing SE, Danish text seem to refer to it with the English term. Danish speakers, if you own a text book that says otherwise – please do correct me!

 

 

 

Maarit Mäenpää
Statistician

Dept. of Agroecology
Aarhus University
Blichers Allé 20, Postboks 50
DK-8830 Tjele

Web: agro.au.dk

Phone: 51 335 104

E-mail: m.maenpaa@agro.au.dk                         

 

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