I am looking for a few rules of thumb of when to determine that my data is 'normal enough' to use a t-test vs. a Mann-Whitney U-test.
From what I have read, most real world data sets are non-normal, and when sample sizes are large, tests including the Shaprio-Wilk will always reject the null hypothesis. I also know that you can look at the Q-Q plot to determine normality, although this seems to be more of a guesstimate than a hard and fast normal vs. non-normal.
Just a few examples to comment on - would you say Q-Q 1 is normal? What about Q-Q 2?