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 2 added 29 characters in body edited Dec 30 '15 at 19:36 Alexey Grigorev 6,61433 gold badges2121 silver badges3737 bronze badges Like @Glen_b said, you can compare your data with the data you're sure is normal - the data you generated yourself, and then rely on your gut feeling :) The following is an example from OpenIntro Statistics textbook Let's have a look at this Q-Q Plot: Is it normal? Let's compare it with normally distributed data: This one looks better than our data, so our data doesn't seem normal. Let's make sure by simulating it several times and plotting side-by-side So our gut feeling tells us that the sample is not likely to be distributed normally. Here's the R code to do this load(url("http://www.openintro.org/stat/data/bdims.RData")) fdims = subset(bdims, bdims$sex == 0) qqnorm(fdims$$wgt, col=adjustcolor("orange", 0.4), pch=19) qqline(fdims$$wgt) qqnormsim = function(dat, dim=c(2,2)) { par(mfrow=dim) qqnorm(dat, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Data)") qqline(dat) for (i in 1:(prod(dim) - 1)) { simnorm = rnorm(n=length(dat), mean=mean(dat), sd=sd(dat)) qqnorm(simnorm, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Sim)") qqline(simnorm) } par(mfrow=c(1, 1)) } qqnormsim(fdims$wgt) load(url("http://www.openintro.org/stat/data/bdims.RData")) fdims = subset(bdims, bdims$sex == 0) qqnorm(fdims$$wgt, col=adjustcolor("orange", 0.4), pch=19) qqline(fdims$$wgt) qqnormsim = function(dat, dim=c(2,2)) { par(mfrow=dim) qqnorm(dat, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Data)") qqline(dat) for (i in 1:(prod(dim) - 1)) { simnorm = rnorm(n=length(dat), mean=mean(dat), sd=sd(dat)) qqnorm(simnorm, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Sim)") qqline(simnorm) } par(mfrow=c(1, 1)) } qqnormsim(fdims$wgt)  Like @Glen_b said, you can compare your data with the data you're sure is normal - the data you generated yourself, and then rely on your gut feeling :) The following is an example from OpenIntro Statistics textbook Let's have a look at this Q-Q Plot: Is it normal? Let's compare it with normally distributed data: This one looks better than our data, so our data doesn't seem normal. Let's make sure by simulating it several times and plotting side-by-side So our gut feeling tells us that the sample is not likely to be distributed normally. Here's the R code to do this load(url("http://www.openintro.org/stat/data/bdims.RData")) fdims = subset(bdims, bdims$sex == 0) qqnorm(fdims$$wgt, col=adjustcolor("orange", 0.4), pch=19) qqline(fdims$$wgt) qqnormsim = function(dat, dim=c(2,2)) { par(mfrow=dim) qqnorm(dat, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Data)") qqline(dat) for (i in 1:(prod(dim) - 1)) { simnorm = rnorm(n=length(dat), mean=mean(dat), sd=sd(dat)) qqnorm(simnorm, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Sim)") qqline(simnorm) } par(mfrow=c(1, 1)) } qqnormsim(fdims$wgt)  Like @Glen_b said, you can compare your data with the data you're sure is normal - the data you generated yourself, and then rely on your gut feeling :) The following is an example from OpenIntro Statistics textbook Let's have a look at this Q-Q Plot: Is it normal? Let's compare it with normally distributed data: This one looks better than our data, so our data doesn't seem normal. Let's make sure by simulating it several times and plotting side-by-side So our gut feeling tells us that the sample is not likely to be distributed normally. Here's the R code to do this load(url("http://www.openintro.org/stat/data/bdims.RData")) fdims = subset(bdims, bdims$sex == 0) qqnorm(fdims$$wgt, col=adjustcolor("orange", 0.4), pch=19) qqline(fdims$$wgt) qqnormsim = function(dat, dim=c(2,2)) { par(mfrow=dim) qqnorm(dat, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Data)") qqline(dat) for (i in 1:(prod(dim) - 1)) { simnorm = rnorm(n=length(dat), mean=mean(dat), sd=sd(dat)) qqnorm(simnorm, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Sim)") qqline(simnorm) } par(mfrow=c(1, 1)) } qqnormsim(fdims$wgt)  1 answered Aug 7 '14 at 17:41 Alexey Grigorev 6,61433 gold badges2121 silver badges3737 bronze badges Like @Glen_b said, you can compare your data with the data you're sure is normal - the data you generated yourself, and then rely on your gut feeling :) The following is an example from OpenIntro Statistics textbook Let's have a look at this Q-Q Plot: Is it normal? Let's compare it with normally distributed data: This one looks better than our data, so our data doesn't seem normal. Let's make sure by simulating it several times and plotting side-by-side So our gut feeling tells us that the sample is not likely to be distributed normally. Here's the R code to do this load(url("http://www.openintro.org/stat/data/bdims.RData")) fdims = subset(bdims, bdims$sex == 0) qqnorm(fdims$$wgt, col=adjustcolor("orange", 0.4), pch=19) qqline(fdims$$wgt) qqnormsim = function(dat, dim=c(2,2)) { par(mfrow=dim) qqnorm(dat, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Data)") qqline(dat) for (i in 1:(prod(dim) - 1)) { simnorm = rnorm(n=length(dat), mean=mean(dat), sd=sd(dat)) qqnorm(simnorm, col=adjustcolor("orange", 0.4), pch=19, cex=0.7, main="Normal QQ Plot (Sim)") qqline(simnorm) } par(mfrow=c(1, 1)) } qqnormsim(fdims$wgt)