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I have a large sample of experimental observations for different categories (specifically, the runtime of an algorithm in different scenarios). I want to plot the mean runtime for each category/scenario and also show the 95% confidence interval using R.

According to the central limit theorem, the mean of each category should be normally distributed (because it is based on a large number of independent observations).

I know how to plot the means as scatter plot and how to add error bars. I'm just unsure about the 95% confidence interval. The 95% confidence interval is the interval in which a new value lays with 95% probability? Or is only the actual mean in the interval with 95% probability?

I found this code on calculating the confidence interval:

error <- qnorm(0.975)*sd/sqrt(n)

Where n is the sample size and sd is the standard deviation. Unfortunately, it lacks further explanation. What exactly is qnorm(0.975) and why do we choose 0.975 to get the 95% confidence interval?

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  • $\begingroup$ Requests for statistical tutoring belong on CrossValidated.com $\endgroup$
    – DWin
    Commented Feb 2, 2017 at 18:23
  • $\begingroup$ Also see ?qnorm and maybe the Wikipedia Simple English page for confidence intervals. While this is certainly topically more appropriate for crossvalidated, I think they appreciate users who do a bit of background reading as much as we do. $\endgroup$
    – Gregor Thomas
    Commented Feb 2, 2017 at 18:28

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qnorm is the quantile function for the normal distribution. More details are available by typing ?qnorm. You pick 0.975 to get a two-sided confidence interval. This gives 2.5% of the probability in the upper tail and 2.5% in the lower tail, as in the picture. Two-Tailed normal distribution

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The 95% confidence interval is the interval in which a new value lays with 95% probability?

No. If you sample very often and compute a 95%-CI every time, than the true value will be within 95% of those confidence intervalls. Sound disturbing? It is.

The standard deviation of the mean is called it's 'standard error'.

The qnorm-part has been explained by G5W.

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  • $\begingroup$ With "true value" you mean the actual mean as opposed to the sample mean, right? $\endgroup$
    – stefanbschneider
    Commented Feb 4, 2017 at 8:50
  • $\begingroup$ Right. Be carefull not to mix standard deviation of a distribution and standard error of the mean up. They are very different. If you want to go deeper into what a confidence intercal is and is not - if you really, really want that, read the "cookie" answer from Keith Winstein in this thread: stats.stackexchange.com/questions/2272/… $\endgroup$
    – Bernhard
    Commented Feb 4, 2017 at 17:20
  • $\begingroup$ Is there a difference between the standard error of the mean and the confidence interval? $\endgroup$
    – stefanbschneider
    Commented Feb 4, 2017 at 19:03
  • $\begingroup$ The standard error of the mean is a single number and the confidence interval consists of two (a lower and an upper border)? $\endgroup$
    – Bernhard
    Commented Feb 4, 2017 at 22:05
  • $\begingroup$ But error <- qnorm(0.975)*sd/sqrt(n) does compute the confidence interval doesn't it? So it's always a certain deviation from the sample mean in both directions that expresses the confidence interval, e.g., mean +/- 1? Or is it also possible that the confidence interval is not symmetric, e.g., [mean-1, mean+2]? I guess not, as we are assuming a normal distribution of the mean. $\endgroup$
    – stefanbschneider
    Commented Feb 5, 2017 at 12:58

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