Tag Archives: confidence intervals

Independent t-test in Python

In a previous post we learned how to perform an independent t-test in R to determine whether a difference between two groups is important or significant. In this post we will learn how to perform the same test using the Python programming language. Along the way we will learn a few things about t distributions and calculating confidence intervals. dataset.In

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Why we need confidence intervals

At Scientifically Sound, we have reviewed ongoing discussions on the benefits of confidence intervals (CIs) over p values for statistical analysis and reproducibility in research. In a short editorial, the statistician Doug Altman summarised why we need confidence intervals and showed how confidence intervals force investigators to consider sizes of effects. Here are the key points: Two different but complementary

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How are confidence intervals useful in understanding replication?

The growing awareness of the need for reproducibility in research is encouraging, but what does reproducible research actually look like in practice? Marty and I recently had an interesting discussion on what it means for a study’s findings to be independently replicated, and the metrics scientists use to interpret reproducibility. I tend to interpret level of reproducibility using confidence intervals

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How to calculate the confidence interval from a p value

Confidence intervals are widely reported in published research and are usually thought to provide more information than p values from significance tests because confidence intervals indicate how precise an estimate is. Sometimes, however, investigators report an estimate (eg. a mean) and p value, but not the confidence interval about the estimate. In a BMJ statistics note, statisticians Doug Altman and

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What makes effective statistical practice?

Science is about asking questions, getting data and (often) applying statistical methods to use data to answer questions. What are some principles of effective statistical practice that statisticians would like working scientists to know? In the ongoing “Ten Simple Rules” series at PLoS Computational Biology, statisticians Kass and colleagues (2016) present some good advice and guidance. Here is a summary

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Common misinterpretations of statistical tests

Researchers often use statistical tests to test hypotheses and/or infer properties of a population based on properties of a sample. A key idea is that all statistical tests assume a statistical model provides a complete and valid representation of variability in the data, and faithfully reflects how the study was conducted and the phenomena being tested. For example, when we

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