Abstract
Because biological processes are full of variations, statistics will give no certainties only chances. What chances? Chances that hypotheses are true/untrue. What hypotheses? For example:
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1.
our mean effect is not different from a 0 effect,
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2.
it is really different from a 0 effect,
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3.
it is worse than a 0 effect,
where 0 effect means that your new treatment or any other intervention does not work. Statistics is about estimating such chances/testing such hypotheses. Please note that trials often calculate differences between a test treatment and a control treatment, and, subsequently, test whether this difference is larger than 0. A simple way to reduce a study of two groups of data, and, thus, two means to a single mean and single distribution of data, is to take the difference between the two means and compare it with 0. In Chap. 2 we explained that the data of a trial can be described in the form of a normal distribution graph with SEMs on the x-axis, and that this method is adequate for testing various statistical hypotheses. We will now focus on a very important hypothesis, the null-hypothesis. We will try and make a graph of this null-hypothesis, and then assess whether our result is significantly different from the null-hypothesis.
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© 2016 Springer International Publishing Switzerland
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Cleophas, T.J., Zwinderman, A.H. (2016). Null-Hypothesis Testing with Graphs. In: Clinical Data Analysis on a Pocket Calculator. Springer, Cham. https://doi.org/10.1007/978-3-319-27104-0_3
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DOI: https://doi.org/10.1007/978-3-319-27104-0_3
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27103-3
Online ISBN: 978-3-319-27104-0
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