Attached below are statistical analyses we conducted but did not end up using due to a lack of accountability in the results or misinterpretation of data. However, we thought it best to still document the graphs here in case it is of use to future teams, while also preserving any forms of data that may be relevant later.

We conducted the same statistical analysis but had 2 previous variations in how we calculated the concentrations. First, we did not correct the background nor normalize our data when using the AAT Bioquest Four Parameter Logistic (4PL) Curve Calculator to generate the standard curve. Thus, we did not have any absorbance values that were too low to generate a concentration value.

We ran the ANOVA and got p=0.01075. However, neither the TukeyHSD or pairwise t-tests were able to identify a pair with significant difference.

Next, when we found the right standard curve equation with the background correction, we ran into the problem of having a minimum value that the absorbance value could be. So, just for the purposes of matching the number of trials, we duplicated the readings of the sample. Additionally, for any values that were below the minimum threshold, we either averaged out the other two readings of the absorbance or triplicated the only reading that was above the minimum.

We ran the ANOVA and got p=0.0008037. However, neither the TukeyHSD or pairwise t-tests were able to identify a pair with significant difference. Additionally, we realized that the adjustments we made to match the number of trials was falsifying our data. So, we changed to our final method of setting those concentrations as 0.