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Frans Rodenburg
Frans Rodenburg
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Negative numbers
When you subtract the mean, all differences will be centered around zero. So if you present your data in a barplot, the bars may suddenly be negative. However, how you apply standardization can have quite a different effect.

Scale
When you divide by the standard deviation, the data will have variance and standard deviation equal to one. Hence the difference in scale.

My normalized data are normal (by definition...)

By what definition exactly? They just have mean $0$ and variance $1$. They don't look normal to me and there is no reason why standardization would yield normally distributed data.

Normalization vs Standardization
Subtracting the mean and dividing by the standard deviation is usually referred to as standardization (see the standardization tag description). On the other hand, normalization is more often used to describe methods that change values such that they lie within a certain range.

Negative numbers
When you subtract the mean, all differences will be centered around zero. So if you present your data in a barplot, the bars may suddenly be negative. However, how you apply standardization can have quite a different effect.

Scale
When you divide by the standard deviation, the data will have variance and standard deviation equal to one. Hence the difference in scale.

My normalized data are normal (by definition...)

By what definition exactly? They just have mean $0$ and variance $1$. They don't look normal to me and there is no reason why standardization would yield normally distributed data.

Negative numbers
When you subtract the mean, all differences will be centered around zero. So if you present your data in a barplot, the bars may suddenly be negative. However, how you apply standardization can have quite a different effect.

Scale
When you divide by the standard deviation, the data will have variance and standard deviation equal to one. Hence the difference in scale.

My normalized data are normal (by definition...)

By what definition exactly? They just have mean $0$ and variance $1$. They don't look normal to me and there is no reason why standardization would yield normally distributed data.

Normalization vs Standardization
Subtracting the mean and dividing by the standard deviation is usually referred to as standardization (see the standardization tag description). On the other hand, normalization is more often used to describe methods that change values such that they lie within a certain range.

Source Link
Frans Rodenburg
Frans Rodenburg
  • 14.3k
  • 3
  • 33
  • 76

Negative numbers
When you subtract the mean, all differences will be centered around zero. So if you present your data in a barplot, the bars may suddenly be negative. However, how you apply standardization can have quite a different effect.

Scale
When you divide by the standard deviation, the data will have variance and standard deviation equal to one. Hence the difference in scale.

My normalized data are normal (by definition...)

By what definition exactly? They just have mean $0$ and variance $1$. They don't look normal to me and there is no reason why standardization would yield normally distributed data.