Tag Archives: statistics

Within-group analyses cannot be used to make between-group comparisons

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

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R: Visualize and calculate a Pearson correlation

Scientists are often interested in understanding the relationship between two variables. One simple way to understand and quantify a relationship between two variables is correlation analysis. Assumptions. This post assumes you understand the theory behind correlation analysis and have a working knowledge of R; it focuses on how to run this type of analysis in R. The dataset: foot length

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The limitations of p values

A recent Nature commentary highlighted a statement by the American Statistical Association on principles to guide the use of p values for interpretation of research findings. The statement was issued out of concern for the lack of understanding of p values and what they imply. Specifically, the 6 principles of the statement are: P values can indicate how incompatible the

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Statistics note: What are confidence intervals?

In the previous post we learned that the standard error of a sample statistic indicates how precisely a population parameter is estimated, and the standard error depends on sample size. For a given standard deviation, a small standard error is obtained when sample size is large. The standard error indicates how variable the sample statistic is if the experiment is

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Statistics note: How does sample size affect precision of estimates?

In the previous post we learned that a sample statistic (e.g., a sample mean) is used to estimate a population parameter (e.g., the population mean), and the standard error of the sample statistic indicates the amount of precision around the estimate of the population parameter. A small standard error indicates that a sample statistic estimates a population parameter with high

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Statistics note: What is statistical inference?

In the previous post we learned that inferential statistics uses information from individuals observed in a sample to provide information about other individuals in the population that were not observed. A description of the population provides information about all individuals. But how is a sample or population described, and how are sample data used to make inferences about a population?

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