In the following tables n1 is the size of the smaller sample and n2 is the size of the larger sample. See Wilcoxon Rank-Sum Test for details. Note that α for a one-tail test (lower tail) is equivalent to 2α for a two-tail test.

**Alpha = .01 (two-tailed)**

**Alpha = .05 (two-tailed)**

**Alpha = .20 (two-tailed)**

Hi Charlez,

I am little confused, why the result of table is different from table in page 1097 in this link: http://www.stat.ufl.edu/~athienit/Tables/tables.pdf

Hi Sadegh,

There is some fluctuation between statistical tables from different sources. I checked the TL values at alpha = .05, two-tails, for n2 = 10. The values in the table in the site you referenced are generally 1 unit higher than in the table on my site. I don’t know why the two sources got different values. The values on my site appear in many references, including Howell’s textbook, Statistical Methods for Psychology.

Charles

Thanks for reply. I will take a look at Howell’s textbook.

good luck!

Hello Sir,

My n1 and n2 are 50. I dont know how to find value corresponding to these values in table.

Please help me.

Navdeep,

For values above 20, you don’t need to use the table. Instead you should use the normal approximation as described on the website.

Charles

If I have a sample size of 9 and 18 can I still use the ‘=NORM.DIST(A1,A6, B8, TRUE) function described for calculating P values for larger sample sizes?

Chrissy,

The normal approximation is probably ok, but it is marginal. I wouldn’t be surprised that the result is more or less the same with that using the table of critical values.

Charles

Thanks very much for your comment, also your whole webpage has helped me no end so thanks very much, it’s a brilliant resource!

When I used the NORM.DIST function my W’ value was larger than W (so I actually used 1- NORM.DIST…) and Wcrit value was more or less the same as my W’ value, rather than W. Presumably this is okay?

Many thanks

Chrissy,

I am pleased that I have been able to help you.

Probably this is ok, but I’d have to see the details to say for sure.

Charles