In the previous post you learned about Git branches. Specifically, you learned about how branches could be used to make changes or additions to files in secret. The original versions of the files that are presentable (manuscripts, thesis, etc) or functional (computer programs) remain available as you make your changes on a branch. The beauty of this approach is that
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We last saw how to write a function in Python. Let’s take a step back to consider how a function works with variables provided to it. Functions can accept input values and return new values. In Python, input values are passed into the function using local variables inserted in the parentheses (). For example, the fahr_to_kelvin() function below receives the
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A function is a named block of code that is used to perform computations. Python provides many in-built functions such as print() and zip(), but functions can also be created by users. Why create functions? Functions provide a way to package code so it is easy to reuse when the same action needs to be performed repeatedly. This means there
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Bar and line graphs with standard deviation (SD) or standard error (SE) error bars are often used to visually present continuous data from laboratory studies with small sample sizes. Last year the journal PloS Biology published a paper showing that presenting such data this way is problematic for the following reasons: bar and line graphs conceal how data are distributed
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Scientists are often interested in understanding the relationship between two variables. One simple way to understand and quantify a relationship between two variables is correlation analysis. Assumptions. This post assumes you understand the theory behind correlation analysis and have a working knowledge of R; it focuses on how to run this type of analysis in R. The dataset: foot length
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A recent Nature commentary highlighted a statement by the American Statistical Association on principles to guide the use of p values for interpretation of research findings. The statement was issued out of concern for the lack of understanding of p values and what they imply. Specifically, the 6 principles of the statement are: P values can indicate how incompatible the
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