Science and computers go hand in hand. But so do humans and errors. That is why in my previous post I talked about my experience of how science, a human endeavour, does not always apply due diligence when it comes to ensuring errors are avoided. I don’t think this is intentional, but rather a mix of ignorance, bias, and lack
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In this, the digital age, we rely heavily on computers. We use them to store, share and analyse our data. We use them to write and submit our scientific manuscripts. We use them to make figures and present our results. We use them to access others research. Yes, we scientists use computers a lot. But are we infallible in our
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T-tests and analysis of variance (ANOVA) are common statistical tests in physiology and biomedical science. While the SAMPL guidelines for reporting statistical analyses and methods in published literature state authors should “describe statistical methods with enough detail to enable a knowledgeable reader with access to the original data to verify the reported results”, such recommendations are rarely implemented. Simply stating
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I assume that, if you are reading this post, you are familiar with research reproducibility and the issues that surround it. Nevertheless, you might not be familiar with the difficulties early career researchers face in this era of open science. A recent paper published in PLOS Biology by Allen & Meller entitled Open science challenges, benefits and tips in early
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Most introductory statistics courses include a section explaining Type I (false positive) and Type II (false negative) errors in hypothesis testing. If you have been through such courses, you would have learned that the tolerance for Type I error is set by the significance level (alpha =0.05; the usual default) while Type II error is controlled by statistical power which
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Academics and researchers must apply for grant funding. These grants provide the financial support required to carry out the research proposed in the grant. These funds are used to purchase equipment, pay salaries, cover publication and travel costs, etc. Being successful in a national funding competition is a key indicator of success. Not only can the researchers carry out the
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