Bias with Western Blot quantification on Image J

As a molecular scientist, I perform a lot of western blots. As you may know, western blots are pretty useless unless you can quantify the difference between the lanes. Image J (or FIJI) is frequently utilized to quantify the results from western blots. However, I have always been weary that there are multiple ways to introduce bias into your quantification with this program. I have wondered if the same person were to analyze the same western blot multiple times, would they get the same values each time? To explore this, I decided to utilize an old western blot image and see what different values I could get without trying to be biased.
To start, I utilized the western blot below. Since this was just an exercise of intrigue, i only analyzed the top bands on the last three columns.

To use Image J, you must select the areas that you want to quantify:

I believe that this first step can cause variability. If a researcher were to make the selected area more narrow or more wide, they may pick up noise surrounding it and make their signal seem greater than it is. To examine this further, I selected those bands three different times and produced the following intensity graphs:

"Attempt 1""Attempt 2"

"Attempt 3"

As of now, there does not look like too much of a difference between these results. To quantify your results on Image J, you use a selection tool to calculate the area under the curve. After quantification, I performed a two-way ANOVA with Tukey's multiple comparisons. I wanted to see if 1) the quantification of the same lane altered between "attempts" and 2) if the relationship between the lanes was the same throughout the different "attempts". It is important to note that the following quantifications do not take noise from the western blot into account.

I was quite surprised to find that the three attempts significantly differed from one another. However, the relationships between the lanes was preserved. 

As mentioned, this method of quantifying did not take noise into account. To correct for noise, Image J allows you to draw a line at the level of noise to act as a threshold. The problem with this is that you cannot really standardize it. Typically, a researcher has to "eyeball" the average level of the noise. To complete this section, I attempted to be as unbiased as possible.The plots will then look like this:

"Attempt One" "Attempt Two"

"Attempt Three"

I then took the area under the curve for these noise-corrected plots and performed another two-way ANOVA with Tukey's multiple comparisons. 

With these normalized values, there was NOT a significant difference between the values for each attempt. Interestingly, normalizing the values revealed a difference between Lane 2 and Lane 3 that were not significant prior to normalization. 

Obviously, we cannot overreach these observations to all wester blot analyses. It is safe to assume that to be as accurate and unbiased as possible, one most control for noise in there western blots. However, I was very surprised that the effect betweens the lanes was preserved amongst all three "attempts." I would like more examples demonstrating this before I get too comfortable with western blot analysis. The next step to examine bias with Image J analysis would be to have multiple people analyze the same western blot. Until then, we must all be careful when analyzing western blots and try to be as consistent as possible.