I have ran a QQ plot on R on my data using

qqnorm(TEDS$LST1); qqline(TEDS$LST1)

which gave me this: enter image description here

The histogram of the data showed a positive skew to left, but I do not know how to interpret the QQ plot. Why are the data points clustering along the line? and does the straight tail at the bottom signify the left skew that I see in the histogram?


3 Answers 3


This QQ plot has the following salient features:

  1. The stairstep pattern, in which only specific, separated heights ("sample quantiles") are attained, shows the data values are discrete. Almost all are whole numbers from $3$ through $21$. A few half-integers appear. Evidently some form of rounding has occurred.

  2. Because the extreme "theoretical quantiles" are at $\pm 3.2$ (roughly), there must be around $1400$ data shown. This is because the extremes for this much Normally distributed data would have Z-scores about $\pm 3.2$. (This estimate of $1400$ is rough, but it's in the right ballpark.)

  3. There is a large number of values at the minimum of $3$, far more than any other value. This is characteristic of left censoring, whereby any value less than a threshold ($3$) is replaced by an indicator that it is less than that threshold--and, for plotting purposes, all such values are plotted at the threshold. (For more on what censoring does to probability plots, see the analysis at https://stats.stackexchange.com/a/30749.)

  4. Apart from this "spike" at $3$, the rest of the points come fairly close to following the diagonal reference line. This suggests the remaining data are not too far from Normally distributed.

  5. A closer look, though, shows the remaining points are initially slightly lower than the reference line (for values between $5$ and $10$) and then slightly greater (for values between $13$ and $20$) before returning to the line at the end (value $21$). This "curvature" indicates a certain form of non-normality.

This particular kind of curvature is consistent with data that are starting to follow an extreme-value distribution. Specifically, consider the following data-generation mechanism:

  • Collect $k\ge 1$ independent, identically distributed Normal variates and retain just the largest of them.

  • Do that $n = 1400$ times.

  • Left-censor the data at a threshold of $3$.

  • Record their values to two or three decimal places.

  • Round the values to the nearest integer--but don't round any value that is exactly a half-integer (that is, ends in $.500$).

If we set $k=50$ or thereabouts and adjust the mean and standard deviation of those underlying Normal variates to be $\mu = -10$ and $\sigma = 7.5$, we can produce random versions of this QQ plot and most of them are practically indistinguishable from it. (This is an extremely rough estimate; $k$ could be anywhere between $8$ and $200$ or so, and different values of $k$ would have to be matched with different values of $\mu$ and $\sigma$.) Here are the first six such versions I produced:


What you do with this interpretation depends on your understanding of the data and what you want to learn from them. I make no claim that the data actually were created in such a way, but only that their distribution is remarkably like this one.

This is R code to reproduce the figure (and generate many more like it if you wish).

k <- 50
mu <- -10
sigma <- 7.5
threshold <- 3
n <- 1400
# Round most values to the nearest integer, occasionally
# to a half-integer.
rnd <- function(x, prec=300) {
  y <- round(x * prec) / prec
  ifelse(2*y == floor(2*y), y, round(y))
q <- c(0.25, 0.95) # Used to draw a reference line
invisible(replicate(6, {

  # Generate data
  z <- apply(matrix(rnorm(n*k), k), 2, max) # Max-normal distribution
  y <- mu + sigma * z                       # Scale and recenter it
  x <- rnd(pmax(y, threshold))              # Censor and round the values

  # Plot them
  qqnorm(x, cex=0.8)
  m <- median(x)
  s <- diff(quantile(x, q)) / diff(qnorm(q))
  abline(c(m, s))
  #hist(x)    # Histogram of the data
  #qqnorm(y)  # QQ plot of the uncensored, unrounded data

(As Nick Cox also suggests) the distribution is right skew and discrete, but to the right of the spike at 3, is roughly similar to a standard normal truncated below -1 (which is right skew), but with a shorter right tail.

I've made some additional comments on the diagram below:

enter image description here

Here's a frequency plot (a sample pmf) that would yield a Q-Q plot roughly similar to yours:

enter image description here

  • $\begingroup$ Thank you Glen. Very helpful diagram. I have to see how and if transforming the data will give me a normal distribution. $\endgroup$
    – Elham
    Commented Jul 16, 2015 at 13:43
  • 1
    $\begingroup$ You are unlikely to improve on this by transformation. The spike at 3 will remain a spike, regardless of what you transform it to. $\endgroup$
    – Nick Cox
    Commented Jul 16, 2015 at 13:48
  • $\begingroup$ I've added a pmf for a set of data that gives a (roughly) similar Q-Q plot to yours. A monotonic transformation can only change the spacings between the spikes, but the spikes themselves all remain the same height; so the big spike at 3 will always be a big spike at the left end of the distribution. $\endgroup$
    – Glen_b
    Commented Jul 16, 2015 at 15:59
  • $\begingroup$ This is not a Q-Q plot for a truncated normal: it's for a left censored normal distribution (at a threshold of $3$) that has been discretized. The truncated normal would not have the spike at a value of $3$; leaving out that spike would cause the Q-Q plot to be substantially more curved than it is. $\endgroup$
    – whuber
    Commented Jul 16, 2015 at 16:03
  • $\begingroup$ @whuber I didn't say it was a truncated normal, I said that aside from the spike at 3 it was similar to one (that is, if you consider everything above 3, it's like one). However, it really isn't like a censored normal, because the spike at 3 is far too large for that -- this is why I (deliberately) didn't describe it that way $\endgroup$
    – Glen_b
    Commented Jul 16, 2015 at 16:10

Your data are positively skewed, meaning skewed to the right. "Right" or "left" is a matter of the longer, more stretched out, tail in the distribution. The terminology presupposes that you are (imagining) looking at a conventional histogram with a horizontal magnitude scale.

But clearly you have integer values between 3 and 21, hence the appearance of an irregular staircase, except that there are values such as 4.5. You have a prominent spike of values at 3: that should not come as a surprise to you, but we can't tell you why. Similarly, if these are counts, then the absence of 0, 1 and 2 may (or may not) be worth comment.

It's possible, however, that numeric measures of skewness may be negative as a side-effect of the spike.

The values are reminiscent of grades on a test in which most students did poorly, but few were utterly abysmal, and some messy answers provoked compromise marks.

Values in the data that are the same must be plotted at the same horizontal level at various levels on the $y$ axis. The average over samples of the same size from a true Gaussian distribution would all be distinct, so the values on the $x$ axis must be distinct.

The spike alone means that you can't call this distribution "normal". If you thought this distribution would be normal, you need to review your thinking.

  • 1
    $\begingroup$ Thank you for the answer. It makes sense; I knew it was not normal, just couldn't figure out in which way it was not. $\endgroup$
    – Elham
    Commented Jul 16, 2015 at 13:42

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