Loterre: SAGE: type I errors

... > public health > epidemiology > statistics and research methods > hypothesis testing > type I errors

... > clinical medicine > medical decision making > biostatistics and clinical epidemiology > hypothesis testing > type I errors

... > inquiry and research > quantitative research > quantitative data analysis > hypothesis testing > type I errors

... > research methods and evaluation > quantitative research > quantitative data analysis > hypothesis testing > type I errors

... > social psychology research > methods > research design > hypothesis (research) > type I errors

... > social research > research methods and evaluation > research design > hypothesis (research) > type I errors

... > behavior mechanisms > human development > statistics, research methods, and measurement > hypothesis (research) > type I errors

... > social research > research methods and evaluation > concepts & philosophies > hypothesis (research) > type I errors

... > epidemiology > statistics and research methods > hypothesis testing > hypothesis (research) > type I errors

... > medical decision making > biostatistics and clinical epidemiology > hypothesis testing > hypothesis (research) > type I errors

... > quantitative research > quantitative data analysis > hypothesis testing > hypothesis (research) > type I errors

... > research methods and evaluation > concepts & philosophies > theory (research methods) > hypothesis (research) > type I errors

type I errors

In inferential statistics, we state a null and alternative hypothesis, gather data, and compute statistics to decide whether to accept or reject the null hypothesis. Because we compute statistics on a sample in order to make decisions about a population whose parameters are unknown, there is always some probability that our decision will be incorrect. [Source: Encyclopedia of Global Health; Alpha Error]

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