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ProfuselyQuarky
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I'm running raw data and although, visually, the trends are promising, none of it is statistically significant. I was just going to leave it at that because the data was obtained after only 1 year of the experiment and I was just going to say that if treatment continued for a longer period of time, there might be more significant results compared to control. But my PI said to transform the data in order to see if it changes anything. I've never really done that before so I'm working on it but I want to know what the general logic is when it comes to knowing when to continue transforming your data vs just accepting the insignificance and moving on. I.e., I'm slowly forcing the data into becoming more normal since the variances are unequal but the more I do so, the more it strays from what the raw data truly represents, no? I'm not a programmer or data analyst, this is all foreign to me.
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