What are type I and type II errors? - Minitab
Alpha (α) is the probability of making a Type I error while testing two hypotheses. Said otherwise, we make a Type I error when we reject the null hypothesis (in favor of the alternative one) within Neyman-Pearson's theoretical positioning within statistics: See pages that link to and include this page. This value is often denoted α (alpha) and is also called the significance level. When a Common mistake: Confusing statistical significance and practical significance. Connection between Type I error and significance level. There is no general relation between alpha and beta. With the jury exemple, type I errors are more important and so the law process is build.
What are type I and type II errors?
Department of Community Medicine, D. Patil Medical College, Pune -India. This article has been cited by other articles in PMC. Abstract Hypothesis testing is an important activity of empirical research and evidence-based medicine.
A well worked up hypothesis is half the answer to the research question. For this, both knowledge of the subject derived from extensive review of the literature and working knowledge of basic statistical concepts are desirable. The present paper discusses the methods of working up a good hypothesis and statistical concepts of hypothesis testing. Many scientists, even those who do not usually read books on philosophy, are acquainted with the basic principles of his views on science.
Popper makes the very important point that empirical scientists those who stress on observations only as the starting point of research put the cart in front of the horse when they claim that science proceeds from observation to theory, since there is no such thing as a pure observation which does not depend on theory. The first step in the scientific process is not observation but the generation of a hypothesis which may then be tested critically by observations and experiments.
It is logically impossible to verify the truth of a general law by repeated observations, but, at least in principle, it is possible to falsify such a law by a single observation. Repeated observations of white swans did not prove that all swans are white, but the observation of a single black swan sufficed to falsify that general statement Popper, It should be simple, specific and stated in advance Hulley et al.
Hypothesis should be simple A simple hypothesis contains one predictor and one outcome variable, e.
Type I and II Errors
Here the single predictor variable is positive family history of schizophrenia and the outcome variable is schizophrenia. A complex hypothesis contains more than one predictor variable or more than one outcome variable, e.
Here there are 2 predictor variables, i. Complex hypothesis like this cannot be easily tested with a single statistical test and should always be separated into 2 or more simple hypotheses. Hypothesis should be specific A specific hypothesis leaves no ambiguity about the subjects and variables, or about how the test of statistical significance will be applied.
This is a long-winded sentence, but it explicitly states the nature of predictor and outcome variables, how they will be measured and the research hypothesis. Often these details may be included in the study proposal and may not be stated in the research hypothesis. However, they should be clear in the mind of the investigator while conceptualizing the study.
Hypothesis should be stated in advance The hypothesis must be stated in writing during the proposal state.
The habit of post hoc hypothesis testing common among researchers is nothing but using third-degree methods on the data data dredgingto yield at least something significant. This leads to overrating the occasional chance associations in the study.
The null hypothesis is the formal basis for testing statistical significance. By starting with the proposition that there is no association, statistical tests can estimate the probability that an observed association could be due to chance. The proposition that there is an association — that patients with attempted suicides will report different tranquilizer habits from those of the controls — is called the alternative hypothesis.
The alternative hypothesis cannot be tested directly; it is accepted by exclusion if the test of statistical significance rejects the null hypothesis.
One- and two-tailed alternative hypotheses A one-tailed or one-sided hypothesis specifies the direction of the association between the predictor and outcome variables. The prediction that patients of attempted suicides will have a higher rate of use of tranquilizers than control patients is a one-tailed hypothesis. A two-tailed hypothesis states only that an association exists; it does not specify the direction. The prediction that patients with attempted suicides will have a different rate of tranquilizer use — either higher or lower than control patients — is a two-tailed hypothesis.
The word tails refers to the tail ends of the statistical distribution such as the familiar bell-shaped normal curve that is used to test a hypothesis. One tail represents a positive effect or association; the other, a negative effect. A one-tailed hypothesis has the statistical advantage of permitting a smaller sample size as compared to that permissible by a two-tailed hypothesis.
Unfortunately, one-tailed hypotheses are not always appropriate; in fact, some investigators believe that they should never be used.
However, they are appropriate when only one direction for the association is important or biologically meaningful. An example is the one-sided hypothesis that a drug has a greater frequency of side effects than a placebo; the possibility that the drug has fewer side effects than the placebo is not worth testing. Whatever strategy is used, it should be stated in advance; otherwise, it would lack statistical rigor. Data dredging after it has been collected and post hoc deciding to change over to one-tailed hypothesis testing to reduce the sample size and P value are indicative of lack of scientific integrity.
If the consequences of a Type I error are not very serious and especially if a Type II error has serious consequencesthen a larger significance level is appropriate.
Two drugs are known to be equally effective for a certain condition. They are also each equally affordable. However, there is some suspicion that Drug 2 causes a serious side-effect in some patients, whereas Drug 1 has been used for decades with no reports of the side effect.
The null hypothesis is "the incidence of the side effect in both drugs is the same", and the alternate is "the incidence of the side effect in Drug 2 is greater than that in Drug 1. So setting a large significance level is appropriate. See Sample size calculations to plan an experiment, GraphPad.
Sometimes there may be serious consequences of each alternative, so some compromises or weighing priorities may be necessary. The trial analogy illustrates this well: Which is better or worse, imprisoning an innocent person or letting a guilty person go free? Trying to avoid the issue by always choosing the same significance level is itself a value judgment. Sometimes different stakeholders have different interests that compete e.
Similar considerations hold for setting confidence levels for confidence intervals.
Claiming that an alternate hypothesis has been "proved" because it has been rejected in a hypothesis test. This is an instance of the common mistake of expecting too much certainty. There is always a possibility of a Type I error; the sample in the study might have been one of the small percentage of samples giving an unusually extreme test statistic.
This is why replicating experiments i. The more experiments that give the same result, the stronger the evidence.