Tag Archives: statistics

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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Independent t-test in R

As scientists, we often want to know if the difference between two groups is important or significant. For example, you may have data on leg strength from students who came to class wearing dress shoes or running shoes. How would you decide if there was a difference in strength between these two groups? How would you quantify the size of

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Minimising false positives and false negatives in research

In December 2016, the Journal of Applied Physiology commenced the series Cores of Reproducibility in Physiology (CORP) to highlight the lack of reproducibliity in physiology research and provide solutions. In the first CORP article, statistician Dogulas Curran-Everett explains that to improve reproducibility in research, experiments and analyses need to minimise false positive and false negative findings. The false positive rate

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P values and hypothesis tests cannot indicate the size or precision of effects

P values and hypothesis testing methods are frequently misused in clinical and experimental research, perhaps because of the misconception that they provide simple, objective tools to separate true from untrue facts. In a new paper, the cardiologist Daniel Mark and statisticians Kerry Lee and Frank Harrell explain the role and limitations of p values and hypothesis tests in clinical research.

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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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Adjusting for differences at baseline in controlled trials

In randomised trials or repeated-measures experimental studies of randomised conditions, researchers often measure a continuous variable at baseline and at the end of the study at follow up. Examples of some outcomes include blood pressure, pain, physiological responses, range of motion, etc. In a BMJ statistics note, methodologists Andrew Vickers and Doug Altman explain how these outcomes can be analysed.

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