# Relationship between the alpha level and type i error

### Alpha - Type I error - WikiofScience

Type I error, also known as a “false positive”: the error of rejecting a null hypothesis If the significance level for a given experiment is α, the experimentwise on the correlation structure among tests), i.e. will in actuality result in a true alpha of. Alpha (α) is the probability of making a Type I error while testing two The alpha level also informs us of the specificity (= 1 - α) of a test (ie, the. The probability of committing a type I error (rejecting the null hypothesis true) is called α (alpha) the other name for this is the level of an association of a given effect size between Tamiflu and psychosis.

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.

### Type I and type II errors - Wikipedia

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.

Null Hypothesis, p-Value, Statistical Significance, Type 1 Error and Type 2 Error

The more experiments that give the same result, the stronger the evidence. There is also the possibility that the sample is biased or the method of analysis was inappropriate ; either of these could lead to a misleading result.

This could be more than just an analogy: Consider a situation where the verdict hinges on statistical evidence e.

This is consistent with the system of justice in the USA, in which a defendant is assumed innocent until proven guilty beyond a reasonable doubt; proving the defendant guilty beyond a reasonable doubt is analogous to providing evidence that would be very unusual if the null hypothesis is true. There are at least two reasons why this is important.

First, the significance level desired is one criterion in deciding on an appropriate sample size.

Second, if more than one hypothesis test is planned, additional considerations need to be taken into account. See Multiple Inference for more information. The answer to this may well depend on the seriousness of the punishment and the seriousness of the crime. For example, if the punishment is death, a Type I error is extremely serious. 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.

Because the investigator cannot study all people who are at risk, he must test the hypothesis in a sample of that target population. No matter how many data a researcher collects, he can never absolutely prove or disprove his hypothesis.

There will always be a need to draw inferences about phenomena in the population from events observed in the sample Hulley et al. The absolute truth whether the defendant committed the crime cannot be determined.

## What are type I and type II errors?

Instead, the judge begins by presuming innocence — the defendant did not commit the crime. The judge must decide whether there is sufficient evidence to reject the presumed innocence of the defendant; the standard is known as beyond a reasonable doubt.

A judge can err, however, by convicting a defendant who is innocent, or by failing to convict one who is actually guilty. In similar fashion, the investigator starts by presuming the null hypothesis, or no association between the predictor and outcome variables in the population.

Based on the data collected in his sample, the investigator uses statistical tests to determine whether there is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis that there is an association in the population.

The standard for these tests is shown as the level of statistical significance. The defendant did not commit crime Null hypothesis: No association between Tamiflu and psychotic manifestations Guilt: The defendant did commit the crime Alternative hypothesis: There is association between Tamiflu and psychosis Standard for rejecting innocence: Beyond a reasonable doubt Standard for rejecting null hypothesis: Convict a criminal Correct inference: Conclude that there is an association when one does exist in the population Correct judgment: Acquit an innocent person Correct inference: Conclude that there is no association between Tamiflu and psychosis when one does not exist Incorrect judgment: Convict an innocent person.

Incorrect inference Type I error: Conclude that there is an association when there actually is none Incorrect judgment: Acquit a criminal Incorrect inference Type II error: Sometimes, by chance alone, a sample is not representative of the population.

Thus the results in the sample do not reflect reality in the population, and the random error leads to an erroneous inference. A type I error false-positive occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error false-negative occurs if the investigator fails to reject a null hypothesis that is actually false in the population.

Although type I and type II errors can never be avoided entirely, the investigator can reduce their likelihood by increasing the sample size the larger the sample, the lesser is the likelihood that it will differ substantially from the population. False-positive and false-negative results can also occur because of bias observer, instrument, recall, etc. Errors due to bias, however, are not referred to as type I and type II errors. Such errors are troublesome, since they may be difficult to detect and cannot usually be quantified.

EFFECT SIZE The likelihood that a study will be able to detect an association between a predictor variable and an outcome variable depends, of course, on the actual magnitude of that association in the target population. Unfortunately, the investigator often does not know the actual magnitude of the association — one of the purposes of the study is to estimate it.

Instead, the investigator must choose the size of the association that he would like to be able to detect in the sample. This quantity is known as the effect size.

### The relationship between alpha and the type one error. | alreadyconscious

Selecting an appropriate effect size is the most difficult aspect of sample size planning. Sometimes, the investigator can use data from other studies or pilot tests to make an informed guess about a reasonable effect size.

Thus the choice of the effect size is always somewhat arbitrary, and considerations of feasibility are often paramount. When the number of available subjects is limited, the investigator may have to work backward to determine whether the effect size that his study will be able to detect with that number of subjects is reasonable. Depending on whether the null hypothesis is true or false in the target population, and assuming that the study is free of bias, 4 situations are possible, as shown in Table 2 below.

Table 2 Truth in the population versus the results in the study sample: