## Tag Archives: effect size

## Research concepts: Confidence interval of a proportion

Data which exist in categories that only have 2 possible values are known as binary data. “Yes” or “No” survey responses, dead or alive, male or female, etc. are examples of binary values. These data can be expressed as proportions (e.g. the proportion of male students in a class), are known as binomial variables, and follow a binomial distribution. The

Read more## Statistics you are interested in: simple linear regression – part 3

In the first and second posts of this series, we performed simple linear regression of a continuous outcome on a single continuous predictor, but we also learned it is possible to include binary or categorical predictors in such regression models. How is this be done? The hsb2.csv dataset we have been using also contains the variable female where male participants

Read more## Statistics you are interested in: simple linear regression – part 2

In the previous post, we performed simple linear regression of science scores on reading scores from 200 students using ordinary least squares (OLS) estimation. This was done using Python’s Statsmodels package. What does the OLS output show and how should it be interpreted? Here is the figure of the individual subject data and the line of best fit, as well

Read more## The impact of statistical power on effect size estimates

In a previous post, we saw that the number of subjects or samples in our study does not influence the rate of false-positive findings. In this post we will learn how sample size influences estimates of the size of studied effects. If the true effect of a medication is to reduce heart rate by 10 beats per minute, how well

Read more## The Critical thinking and Appraisal Resource library (CARL) to understand and assess treatment claims

Every day, we are confronted by claims about effects of treatments, many of which are not supported by evidence and are misleading. It is easy to overestimate the benefits of treatments and to underestimate their potential risks, without knowing how to accurately assess claims about treatments. To address these problems, Castle and colleagues developed the Critical thinking and Appraisal Resource

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