In hypothesis testing, the number of samples needed to find an effect depends on the: size of the effect variability of the effect tolerance to reporting a false positive (Type I error) and probability of not reporting a false negative (1 – probability of Type II error) The following code uses the stats package in R to calculate sample size
Read more
I am assuming you have followed the first and second tutorial in this short series. You should now have a list variable called refs2 in your Python console. If you inspected the first item in refs2 list, you will notice that it specifies the journal, volume and page information for the first reference. 1 ‘1. J Neurosci. 2015 Dec 9;35(49):16159-70.
Read more
Many research questions investigate how outcomes change (over time) in response to different test conditions or treatments. Participants are randomised into groups to receive a test condition or treatment to make the groups comparable in every way except the test condition or treatment that is received. Consequently, comparing outcomes between groups allows us to understand how outcomes change under different
Read more
In the previous post, I explained how I recently had to sort through PubMed references to extract email addresses. As part of the tutorial, I had you download a sample text file containing 14 references, and we read the content of this file in Python. We are now going to continue cleaning the references to have them in a standardized
Read more
My colleagues and I recently needed to identify all the PubMed references on a given topic and locate email addresses of the corresponding authors. The good news is that the Author information section of PubMed references contains one or more email addresses approximately half of the time. This meant that I could automate the extraction of these email addresses by
Read more
Scientifically Sound 