The critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis.the critical region is the region that we reject the null hypothesis
The significance level is usually denoted by the Greek symbol α (lowercase alpha). Popular levels of significance are 10% (0.1), 5% (0.05), 1% (0.01), 0.5% (0.005), and 0.1% (0.001). If a test of significance gives a p-value lower than the significance level α, the null hypothesis is rejected. Such results are informally referred to as 'statistically significant'. For example, if someone argues that "there's only one chance in a thousand this could have happened by coincidence", a 0.001 level of statistical significance is being implied. The lower the significance level chosen, the stronger the evidence required. The choice of significance level is somewhat arbitrary, but for many applications, a level of 5% is chosen by convention.The significance level of a test is the probability that the null hypothesis is rejected when it is true, right?
Wouldn't you normally do it the other way around?can the significance level be calculated by finding the probability of being in the critical region?
Simon Bridge said:The critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis.
http://en.wikipedia.org/wiki/Statistical_hypothesis_testing The significance level is usually denoted by the Greek symbol α (lowercase alpha). Popular levels of significance are 10% (0.1), 5% (0.05), 1% (0.01), 0.5% (0.005), and 0.1% (0.001). If a test of significance gives a p-value lower than the significance level α, the null hypothesis is rejected. Such results are informally referred to as 'statistically significant'. For example, if someone argues that "there's only one chance in a thousand this could have happened by coincidence", a 0.001 level of statistical significance is being implied. The lower the significance level chosen, the stronger the evidence required. The choice of significance level is somewhat arbitrary, but for many applications, a level of 5% is chosen by convention.
http://en.wikipedia.org/wiki/Significance_levelWouldn't you normally do it the other way around?
How would you choose the critical region?
A hypothesis is an educated guess or a proposed explanation for a phenomenon that can be tested through scientific methods.
The purpose of hypothesis testing is to determine whether the data collected supports or rejects the proposed hypothesis.
A hypothesis is tested by collecting and analyzing data, comparing it to the expected outcome based on the hypothesis, and determining if the results support or reject the hypothesis.
A null hypothesis is a statement that assumes there is no significant difference between two or more groups or variables, while an alternative hypothesis is a statement that suggests there is a significant difference between the groups or variables.
The validity of a hypothesis can be determined by conducting a well-designed experiment, collecting and analyzing data, and using statistical methods to determine the significance of the results. The hypothesis is considered valid if the results support it and can be replicated by others.