ANOVA Test in python code and interpretation - 1

 I’d like to know the association btw personal income level(explanatory) and smoking amount(quantitative response). I thought there’s a certain relationship. From my experience, I’ve quit smoking since new year 2016 for raising cigarette prices surprisingly. However, I wouldn’t quit the smokes if I’ve got more paid. 

 From NESARC(’S1Q10A’), I split into 3 levels as ‘low’, 'medium’, 'high’ according to the level of personal income to make it categorical. The 'low’ level was set up to 30%, 'medium’ level was up to 70% and more than 70%(30% top of personal income) are categorized as 'high’.

 ANOVA results revealed that there’re no significant relationship btw personal income and number of cigarettes smoked, F(2, 9345)=1.130 and P-value=0.323. And the belows are means and std for no of cigarettes smoked by personal income level.

means for numcigmo_est by personal income
INCOMECAT        NUMCIGMO_EST
low                       424.175214
medium               413.702265
high                     423.927979

standard deviations for numcigmo_est by personal income
INCOMECAT       NUMCIGMO_EST           
low                      342.501714
medium              311.897224
high                    335.876155

 There’re no significant difference each others and here are the results of post hoc test. Especially, ‘meandiff’ value btw 'high’ and 'low’ is very small. Of course, there’re all 'False’ results in reject columns when the significant level(alpha) is set to 0.05.

Multiple Comparison of Means - Tukey HSD,FWER=0.05
==============================================
group1 group2 meandiff  lower    upper  reject
———————————————-
high   low    0.2472  -20.5833 21.0778 False
high  medium -10.2257 -29.6021  9.1507 False
low   medium -10.4729 -29.5268  8.5809 False
———————————————-

 If someone ask me whether this result are credible or not, I would say 'No’. There might be lots of uncontrolled fators, for one simple example, the different ciga. cost by State.