Sum, Mean, Median and Standard Deviation Using Lists in Java

Well, I needed methods for sum, mean, median, and standard deviation in Java using Lists, so I coded them. Probably is not the most beautiful code that you've ever seen but it works.
import java.util.*;
class methods {
    public int sum (List<Integer> a){
        if (a.size() > 0) {
            int sum = 0;

            for (Integer i : a) {
                sum += i;
            return sum;
        return 0;
    public double mean (List<Integer> a){
        int sum = sum(a);
        double mean = 0;
        mean = sum / (a.size() * 1.0);
        return mean;
    public double median (List<Integer> a){
        int middle = a.size()/2;

        if (a.size() % 2 == 1) {
            return a.get(middle);
        } else {
           return (a.get(middle-1) + a.get(middle)) / 2.0;
    public double sd (List<Integer> a){
        int sum = 0;
        double mean = mean(a);

        for (Integer i : a)
            sum += Math.pow((i - mean), 2);
        return Math.sqrt( sum / ( a.size() - 1 ) ); // sample
class t {
    public static void main (String[]args) {

        methods m = new methods();

        List<Integer> c = Arrays.asList(2,49,11,44,88,1,1,5,33,88,5,44,2,44,44,132,6,2,22,22,5,1,22,22);


4 Responses to Sum, Mean, Median and Standard Deviation Using Lists in Java

  1. Robin says:

    Your mean function has a bug — what happens if all the entries in the input list are negative?

  2. Justin says:

    The standard deviation has 2 bugs with it. The first bug is that int sum = 0 should be a double because it looses the precision and can return inaccurate answers. The second bug is return Math.sqrt(sum / (a.size() – 1)) should not have the -1. Great job over all it helped a lot!

    • Eduardo says:

      Thanks for your comment. Please refer to Bessel’s correction to understand why this a.size-1 is there: “In statistics, Bessel’s correction, named after Friedrich Bessel, is the use of n − 1 instead of n in the formula for the sample variance and sample standard deviation, where n is the number of observations in a sample: it corrects the bias in the estimation of the population variance“. This is the SD of a sample, and therefore it uses the N-1, and that is why there is a comment “sample” on the very same line.

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