When you have insufficient data or your standards are low. If I keep gathering data there will be some point beyond which any statistical test for Normality will reject the null hypothesis. That said, a failure to reject the null hypothesis (data are normally distributed) is not proof that the data are normally distributed. There are graphical approaches, but there too it is mostly a value judgment deciding if the data are close enough to normally distributed.
Randomization during sampling. If u do not ensure this, you are likely to have, for example. scores of students in an examination greatly concentrated below the mean or above the mean. A normal distribution should be spread around/about the mean where you have the same population at both the left and the right sides of the mean to avoid positive or negative skewness
Normalization using rnorm(100) in R programming to have a Bell-Shaped data.
Data is not normally distributed. For data you can give only an empirical, discrete distribution. The normal distribution is a model and as such are simplified, idealized representation of a concept.
In statistics, the distribution is a feature of a random variable, and observations are interpreted as realizations of such random variables. A random variable is a mathematical representation of a data-generating process.
The relevant question is: Is the random variable with the chosen distribution model (e.g. the normal distribution) a useful, sufficiently accurate, representation of a process that may have generated the observed data?
The answer depends on the context in and the purpose for which the model/the representation is used.
Muqdad Bashir Hussein wants to know "When is the raw data normally distributed?".
In my long experience with raw data, which generally is less like raw steak and more like raw roadkill, the answer, as they say in Thailand, is some fine afternoon in your next reincarnation.
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