Research concepts: Confidence interval of a mean

In previous posts, we learned that the aim of statistics is to extrapolate properties of samples to make inferences about population. However, random variation in individuals in the population produces sampling error, which means a single sample may not accurately reflect properties of the population. When data are binary, we learned how the 95% confidence interval (CI) of a sample

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The challenge of open science for early career researchers

I assume that, if you are reading this post, you are familiar with research reproducibility and the issues that surround it. Nevertheless, you might not be familiar with the difficulties early career researchers face in this era of open science. A recent paper published in PLOS Biology by Allen & Meller entitled Open science challenges, benefits and tips in early

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Research concepts: The Normal Distribution

At Scientifically Sound, we have shown how to verify whether data are Normally distributed, and discussed whether it matters that data are Normally distributed. Let’s take a step back and consider what a Normal distribution is. A Normal distribution is a bell-shaped curve observed when the number of data points that occur in a population (y-axis) is plotted against the

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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

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