Editing Misuse of statistics (section)

===Ignoring important features===
Multivariable datasets have two or more [[Feature (machine learning)|features/dimensions]]. If too few of these features are chosen for analysis (for example, if just one feature is chosen and [[simple linear regression]] is performed instead of [[Linear regression#Simple and multiple linear regression|multiple linear regression]]), the results can be misleading. This leaves the analyst vulnerable to any of various [[:Category:Statistical paradoxes|statistical paradoxes]], or in some (not all) cases false causality as below.