## Research concepts: Quantifying scatter In a previous post we used binary data to demonstrate sampling error and calculate 95% confidence intervals (CI). Now, suppose that data can take many values; for example, normal body temperature has many values and varies continuously over a physiological range. How can we measure this variability in body temperature? For continuous data, variability can be quantified as the standard

## Fitting polynomial or exponential curves to biological data in Python Raw data are not always pretty. Data can have different patterns, be noisy, and vary from trial to trial. We usually collect data to measure, or more accurately, estimate an effect or phenomenon. At times, it is necessary to fit a mathematical expression to raw data in order to estimate the underlying effect or phenomenon. We can then use this

## R: Analysing small datasets – Part 3 In the previous post, when repeated measures data from 10 subjects in 2 conditions were compared, it seemed that subjects who took drug 1 slept fewer hours compared to when they took drug 2. How might we test whether the median number of hours of sleep after drug 1 is less than after drug 2? We can calculate the difference

## R: Analysing small datasets – Part 2 In the previous post we plotted repeated measures data from 10 subjects under 2 conditions. There are different ways to analyse small datasets. We could apply parametric methods to analyse the data values, such as describing the data with means and standard deviations, and calculating a paired difference. Or, we could also apply non-parametric methods by analysing data values based 