This blogs provides an overview of linear regression. It is suitable for those with little to no experience of this type of analysis. This is not a guide on how to conduct your own analysis, but instead will serve as a taster to some of the key terms and principles of regression.
‘Evidence-based practice’ is a commonly used phrase. But this blog asks the question: ‘just how much can we trust published scientific literature?’ with particular reference to the problems of publication bias and statistical approaches.
This blog explains what is meant by Type I and Type II errors in statistics. Whereby we can end up with false positive and false negative results.
This blog uses 3 examples to demonstrate that, even though there may be an association between two events or variables, this does not mean that one has caused the other.
This blog is a critical appraisal of a randomised controlled trial looking at the effects of additional therapy on young children with spastic cerebral palsy.
A nuts and bolts tutorial on how to read a forest plot, featuring a couple of exercises so that you can test your own understanding.
Let’s figure out how to get the essential information from a meta-analysis at a glance, by studying a forest plot.
Median has come to be known for its fair reflection in the case of outliers. However, it is not a perfect statistic. Let me tell you about 3 defects the median as a measure of average.
Let’s find out why physicians sometimes contradict each other from a statistical perspective. And see how students can learn from that.
Come with me. I’ll show you the best way to display the efficacy of a drug. And the pitfalls around it. Ladies and gentlemen, welcome to the world of Number needed to treat.
Confused about Hazard Ratios and their confidence intervals? This blog provides a handy tutorial.
This post talks about the real meaning of p-value. No fancy words. No complicated definitions. Only simple notions included.
How can you tell if a variable is nominal, ordinal, or numerical? Why does it even matter? Determining the appropriate variable type used in a study is essential to determining the correct statistical method to use when obtaining your results. It is important not to take the variables out of context because more often than not, the same variable that can be ordinal can also be numerical, depending on how the data was recorded and analyzed. This post will give you a specific example that may help you better grasp this concept.
A description of the two types of data analysis – “As Treated” and “Intention to Treat” – using a hypothetical trial as an example