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Edit: I mentioned this issue in my second paragraph but I want to emphasize the point again, in case it gets forgotten along the way. What usually matters is not whether you can tell something is not-actually-normal (whether by formal test or by looking at a plot) but rather how much it matters for what you would be using that model to do: How sensitive are the properties you care about to the amount and manner of lack of fit you might have between your model and the actual population?

The answer to the question "is the population I'm sampling actually normally distributed" is, essentially always, "no" (you don't need a test or a plot for that), but the question is rather "how much does it matter?". If the answer is "not much at all", the fact that the assumption is false is of little practical consequence. A plot can help some since it at least shows you something of the 'amount and manner' of deviation between the sample and the distributional model, so it's a starting point for considering whether it would matter. However, whether it does depends on the properties of what you are doing (consider a t-test vs a test of variance for example; the t-test can in general tolerate much more substantial deviations from the assumptions that are made in its derivation than an F-ratio test of equality variances can).

[Edit: I just conducted a random poll -- well, I asked my daughter, but at a fairly random time -- and her choice for the least like a straight line was "d". So 100% of those surveyed thought "d" was the most-odd one.]

z = lm(dist~speed,cars)$residual
n = length(z)
xz = cbind(matrix(rnorm(12*n),nr=n),z,matrix(rnorm(12*n),nr=n))
colnames(xz) = c(letters[1:12],"Z",letters[13:24])

opar = par()
par(mfrow=c(5,5));
par(mar=c(0.5,0.5,0.5,0.5))
par(oma=c(1,1,1,1));

ytpos = (apply(xz,2,min)+3*apply(xz,2,max))/4
cn = colnames(xz)

for(i in 1:25) {
  qqnorm(xz[,i],axes=FALSE,ylab= colnames(xz)[i],xlab="",main="")
  qqline(xz[,i],col=2,lty=2)
  box("figure", col="darkgreen")
  text(-1.5,ytpos[i],cn[i])
}

par(opar)

(I've been making sets of plots like this since the mid-80s at least. How can you interpret plots if you are unfamiliar with how they behave when the assumptions hold -- and when they don't?)

[Edit: I just conducted a random poll --- well, I asked my daughter, but at a fairly random time -- and her choice for the least like a straight line was "d". So 100% of those surveyed thought "d" was the most-odd one.]

    z = lm(dist~speed,cars)$residual
    n = length(z)
    xz = cbind(matrix(rnorm(12*n), nr=n), z, 
         matrix(rnorm(12*n), nr=n))
    colnames(xz) = c(letters[1:12],"Z",letters[13:24])
    
    opar = par()
    par(mfrow=c(5,5));
    par(mar=c(0.5,0.5,0.5,0.5))
    par(oma=c(1,1,1,1));
    
    ytpos = (apply(xz,2,min)+3*apply(xz,2,max))/4
    cn = colnames(xz)
    
    for(i in 1:25) {
      qqnorm(xz[, i], axes=FALSE, ylab= colnames(xz)[i], 
             xlab="", main="")
      qqline(xz[,i],col=2,lty=2)
      box("figure", col="darkgreen")
      text(-1.5,ytpos[i],cn[i])
    }
    
    par(opar)

(I've been making sets of plots like this since the mid-80s at least. How can you interpret plots if you are unfamiliar with how they behave when the assumptions hold --- and when they don't?)

See more:

(I've been making sets of plots like this since the mid-80s at least. How can you interpret plots if you are unfamiliar with how they behave when the assumptions hold -- and when they don't?)

See more: