Contingency tables vs Kolmogorov Smirnov I ran an experiment with a treatment group and a control group. All subjects were asked to choose an integer between 1 and 80 (which was the variable of interest), to indicate the amount of money they were willing to pay for an object. There were a total of 180 subjects, so some integers were not chosen.
I want to see whether the distributions were different in the treatment group compared to the control group. Which test is better?
1) The Kolmogorov-Smirnov test (which is strictly speaking for continuous data)
2) Contingency tables with Fisher's exact test? 
 A: Given the context of your data, I recommend the KS test, or a Wilcoxon Rank Sum (aka Mann-Whitney U) test. Please see assumptions: https://statistics.laerd.com/spss-tutorials/wilcoxon-signed-rank-test-using-spss-statistics.php
The use of a Fisher's exact test makes sense if you have two categorical variables producing something something like a 2 x 3 contingency table. From your question, it seems you may potentially have a 2 x 80 contingency table if you treat each monetary value as a level in the Money category by the Group factor. Hence, it doesn't make sense to use a Fisher's exact test for your analysis.
To briefly explain each test:
Kolmogorov-Smirnov

An attractive feature of this test is that the distribution of the K-S test statistic itself does not depend on the underlying cumulative
distribution function being tested. Another advantage is that it is an
exact test (the chi-square goodness-of-fit test depends on an adequate
sample size for the approximations to be valid). Despite these
advantages, the K-S test has several important limitations:

*

*It only applies to continuous distributions.

*It tends to be more sensitive near the center of the distribution than at the tails.

*Perhaps the most serious limitation is that the distribution must be fully
specified. That is, if location, scale, and shape parameters are
estimated from the data, the critical region of the K-S test is no
longer valid. It typically must be determined by simulation.


http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm
Contingency tables with Fisher's exact test

"is used when you want to conduct a chi-square test but one or more of
your cells has an expected frequency of five or less.  Remember that
the chi-square test assumes that each cell has an expected frequency
of five or more, but the Fisher's exact test has no such assumption
and can be used regardless of how small the expected frequency is."

(http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm)
