Statistical tests can be defined as a test that is used to determine the statistical significance of observation and there are two main types of error that can occur. A type I error occurs when a false negative result is obtained in terms of the true null hypothesis which is statistically significant p-value obtained by false-positive measurement techniques. A type II error occurs when a false-positive result is obtained in terms of the null hypothesis which is obtained by a false negative measurement technique.

The probability that these tests will be positive for a true statistic is sometimes called the test's sensitivity and the probability that a test will be negative for a negative statistic is sometimes called the specificity.

It can be considered as a method of making decisions using data from a scientific method and the result is said to be statistically significant if it has been predicted as unlikely to have occurred by luck alone which is according to the pre-determined threshold probability that is the significance level. The term "test of significance" was coined by statistician Ronald Fisher as these tests are used in determining what outcomes of a study would lead to a rejection of the null hypothesis for a pre-specified statistical significance. This can even help to decide whether results contain enough information to cast doubt on conventional wisdom if it is given that the conventional wisdom has been used to establish the null hypothesis. The critical region of a hypothesis statistic test is the set of values for all outcomes which cause the null hypothesis to be rejected. This is rejected in favor of the alternative hypothesis.

It is sometimes called confirmatory data analysis which is in contrast to the exploratory data analysis which may not have pre-specified hypotheses and so statistical hypothesis testing is a key technique of frequency probability. In the Statistical hypothesis tests, it is defined as a procedure that controls (fixes) the demonstrating probability of incorrectly deciding that a default position is incorrect. This procedure is based on how likely it might be for a collection of observations to occur if the null hypothesis were actually true and therefore the probability of creating an incorrect decision based on it is not the probability that the null hypothesis is true.

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