Hello,
My research is about associative learning in worms, I train worms to associate two stimulus and measure their change in behavior
specifically, I expose the worms to chemicals called DA (attractive odor) or to DA together with HCl (aversive stimulus). the behavior test is called chemotaxis test, in which we measure the worms attraction toward DA, the assumption is that worms that where exposed to both DA and HCl will be less attracted to DA following the training, in this figure i'm testing different dilution of DA in the test (all 3 set of columns underwent the same training), and as you can see the smaller the dilution both DA and DA+HCl are Skewed toward more positive values of chemotaxis index (CI) because the DA concentration is higher  the attraction force is greater. my question is which analysis will better help me determine the best DA dilution in the test? me and my PI have difference of opinion regarding the analysis that best suited here. 1) twoway anova followed by multiple comparisons which to my understanding is the default analysis in this case. the problem is in my opinion is that one of the assumption of ANOVA is that distributions have the same variance, because anova create a sort of average variance which all data in compered against. I tested the variance using Levene's test and found pvalue equals 0.180 so that means that technically I can assume equal variance. 2) perform two sample equal variance ttest between each pair, here the result is different as I get the smaller pvalue for 1:100 dilution (probably because this dilution produce better homology in the data)
I think that even though ANOVA is the standard way to analyze similar data in this case it not right because it Skewed the pvalue toward 1:1K dilution probably because this dilution gives the highest difference between the means (but not significantly) compared to the other dilutions, but in this specific case because I'm trying to determine the optimal dilution that will be most effective in the chemotaxis test, isn't it wrong to use anova that average the variance across dilutions and basically nullified the objective? won't a more right way will be to use series of unrelated ttest which will use each dilutions variance independently?
for your convenience I'm attaching the raw data and the two example of analysis
any insight will be most appreciated
thank you
Netanel
My research is about associative learning in worms, I train worms to associate two stimulus and measure their change in behavior
specifically, I expose the worms to chemicals called DA (attractive odor) or to DA together with HCl (aversive stimulus). the behavior test is called chemotaxis test, in which we measure the worms attraction toward DA, the assumption is that worms that where exposed to both DA and HCl will be less attracted to DA following the training, in this figure i'm testing different dilution of DA in the test (all 3 set of columns underwent the same training), and as you can see the smaller the dilution both DA and DA+HCl are Skewed toward more positive values of chemotaxis index (CI) because the DA concentration is higher  the attraction force is greater. my question is which analysis will better help me determine the best DA dilution in the test? me and my PI have difference of opinion regarding the analysis that best suited here. 1) twoway anova followed by multiple comparisons which to my understanding is the default analysis in this case. the problem is in my opinion is that one of the assumption of ANOVA is that distributions have the same variance, because anova create a sort of average variance which all data in compered against. I tested the variance using Levene's test and found pvalue equals 0.180 so that means that technically I can assume equal variance. 2) perform two sample equal variance ttest between each pair, here the result is different as I get the smaller pvalue for 1:100 dilution (probably because this dilution produce better homology in the data)
I think that even though ANOVA is the standard way to analyze similar data in this case it not right because it Skewed the pvalue toward 1:1K dilution probably because this dilution gives the highest difference between the means (but not significantly) compared to the other dilutions, but in this specific case because I'm trying to determine the optimal dilution that will be most effective in the chemotaxis test, isn't it wrong to use anova that average the variance across dilutions and basically nullified the objective? won't a more right way will be to use series of unrelated ttest which will use each dilutions variance independently?
for your convenience I'm attaching the raw data and the two example of analysis
any insight will be most appreciated
thank you
Netanel
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