Should I standardize or normalize variables before conducting a principal components analysis

I am very confused as I am reading through PCA. Some sources say that I should normalize my data before applying PCA, and some sources say that I should standardize my data before applying PCA. I know that normalization will only change the scale of my values into a range of [0,1]. On the other hand, when standardizing, I am changing variables' means to 0 and standard deviations to 1.

Sources say that I should standardize my variables: https://onlinecourses.science.psu.edu/stat505/node/55

Sources say that I should normalize my variables:

https://datafai.com/2017/10/27/data-standardization-or-normalization/

• I've never seen a definition of "normalization" that involves changing the scale of variables onto the range of [0,1]; perhaps that is a terminology that comes up in certain fields, but it is certainly not a widely used definition. And, in fact, the last link you provide (to another CrossValidated question) seems to me to make it clear that they are referring to standardization (e.g. to mean 0 and st.dev. 1). The accepted answer in that thread provides the intuition as to why this is important; that author used the term "normalization" but in the comments clarifies they meant "standardization" Apr 24 '18 at 21:27
• Yes, I found that definition of normalization online. I am just trying to see why are people keep changing their terminologies between normalization and standardization. The last link is about standardization, but the questioner asks for normalization. It also seems the best answer has also confused between normalization and standardization. Wikipedia says 'Feature scaling is used to bring all values into the range [0,1]' under the Normalization (statistics) page. Apr 24 '18 at 21:31
• I don't know who (meaning which individuals) keeps changing their terminologies. What's much more obvious is a culture or group difference: machine learning people are using many terms in senses different from those earlier established in statistics (as well as contributing evocative newer names). There is some terrible terminology in statistics (why do terms such as dependent and independent variables survive?) and naming things after people is double-edged, but normalized is already so overloaded in mathematical sciences that it didn't need another meaning. Apr 24 '18 at 22:09