Various tools, scales and techniques are available to researchers to quantify outcome measures. Some of these tools are familiar, like a weight scale to measure weight loss over the course of an exercise program. Others are less familiar and are only understood by those working in the same field. Furthermore, different outcome measures can be calculated from the same data.
Read more
The Conversation recently published a nice piece by Louis and Chapman on common statistical misinterpretations and how they can be avoided. Here is summary of the main points: Problem Reason Solution 1. Assuming small differences are meaningful Most small differences are due to chance, not meaningful differences Ask for the margin of error (ie. half of the 95% CI): if
Read more
Many areas of neuroscience require computational techniques to process or analyse data. Some journals including the Nature family of journals and BioMed Central are encouraging investigators to share computer code and programs to improve scientific reproducibility, but these practices are not always adopted. For complex projects with multiple collaborators, it may be unclear what or how much can be shared.
Read more
My last two posts have shown how to perform an independent t-test in the R programming language and the Python programming language. For those of you who are familiar with statistics, you likely know that an independent t-test is equivalent to performing an one-way analysis of variance on the data. What you may not have realised is that both these
Read more
Jupyter notebooks are a scientific computing development platform that can run many different programming languages, including Python via the interactive Python interpreter IPython. The key advantage in a notebook environment is that code can be sectioned into cells for testing while retaining all the interactive features of IPython, including magic commands. IPython magics are a specialised set of commands hosted
Read more
Scientifically Sound 