Data analysis for counts What would be the best data analysis to use for the following data? I was thinking of using the Wilcoxin ranked sum but there are so many ties. I have two independent groups and I am just looking to see if the counts differ between the treatment and control conditions.
    Count Condition
       2 Treatment
      36 Treatment
       1 Treatment
      26 Treatment
      11 Treatment
       0 Treatment
      69 Treatment
       5 Treatment
       0 Treatment
       4 Treatment
       1 Treatment
      19 Treatment
       4 Treatment
       0 Treatment
       1 Treatment
      69 Treatment
       2 Treatment
      11 Treatment
      58 Treatment
      12 Treatment
       0 Treatment
       0   Control
      10   Control
       0   Control
      42   Control
      13   Control
      14   Control
       0   Control
      52   Control
      26   Control

Thank you so much!
 A: The counts look over dispersed so I'm going to jump right to a negative binomial model.
Call:
MASS::glm.nb(formula = count ~ group, data = d, init.theta = 0.4053622108, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7517  -1.1621  -0.4610   0.2324   1.2307  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   2.8590     0.5296   5.398 6.72e-08 ***
grouptest    -0.1014     0.6332  -0.160    0.873    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Deviance goodness of fit test shows the model is a good fit.  Confidence interval for test group is (-1.48 1.07).  Not sure if that means anything to you (I would need to know what the data are measuring to preoperly interpret), but the CI seems relatively symmetric about 0, so for every argument that goes "The ci covers an important effect size of x" one could also argue "it also covers -x".
So yea, I don't think there is an effect here.  The data seem really small here tho.  Did you do a power analysis?
