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In a few weeks, I have to make a presentation on my research. When conducting my research, however, I made a small mistake. Unfortunately, I do not have the ability to conduct the experiment again, and I am not sure how to go about acknowledging the mistake in my presentation. All of the tests I have been able to do suggest that this mistake did not impact the results. Furthermore, my research was in physics, and the results conform to well-established, theoretical laws. I know that it is only right for me to acknowledge, but I fear that all of my research will be judged poorly because of this one, simple mistake (that I deeply regret making). How can I go about presenting this? Are my fears warranted?

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    "All of the tests I have been able to do suggest that this mistake did not impact the results" – so you cannot exclude that it did? – n1000 Feb 22 '17 at 21:09
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I am always in favor of the approach of disclosing a mistake and sharing any work you have done towards understanding the impact of that mistake, whether it suggests the original result is still viable or not. For example, depending on the exact type of mistake you made, it might be possible to model the influence on the outcome (e.g. if something is out of focus, but you know how out of focus it was, you can estimate the expected scattering of your resulting data).

Certainly someone can judge you based on whatever their personal criteria for perfection are, but especially if you are an inexperienced researcher I would lose much less respect for you under these circumstances than I would for someone who was going to judge you poorly after making one mistake, acknowledging it, and studying the likely impact of the mistake. Your issue would be one of learning to be a careful researcher; their issue would be one of personal character. Which is easier to resolve?

I would also point out that showing you will acknowledge a mistake in one area of your work does not discredit the other work; if anything, it suggests you have more self-awareness and insight into your data than someone that presents everything as if it is definitively free of all flaws.

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