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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Understanding the meaning of a confidence interval can take a little effort. The key idea is we want to infer findings from a study to subjects who were not part of the study. Sometimes, reading explanations in different words can help. Let’s pause in our series and see how others have explained what confidence intervals mean: Harvey Motulsky: The true
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Data which exist in categories that only have 2 possible values are known as binary data. “Yes” or “No” survey responses, dead or alive, male or female, etc. are examples of binary values. These data can be expressed as proportions (e.g. the proportion of male students in a class), are known as binomial variables, and follow a binomial distribution. The
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Most introductory statistics courses include a section explaining Type I (false positive) and Type II (false negative) errors in hypothesis testing. If you have been through such courses, you would have learned that the tolerance for Type I error is set by the significance level (alpha =0.05; the usual default) while Type II error is controlled by statistical power which
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In doing research, we apply the scientific method to answer questions. For example, does cigarette smoking cause lung cancer? What are the mechanisms of weakness after stroke? Why do cells become cancerous? What properties are specific to the poison of South American tree frogs? We want to understand all the individuals being studied (i.e. people, cells, frogs, etc.) but it
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An important part of conducting sound science involves interpreting data correctly. Unfortunately, we don’t do that very well. For example, we are fooled by regression to the mean, we report findings when there are none, and we are overconfident about statistical power and significance. As scientists and lay persons, we want to be certain about research findings. But statistics only
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