T-Test
Synopsis
This operator is used for comparison of performance vectors. This operator performs a t-test to determine the probability for the null hypothesis i.e. 'the actual means are the same'.
Description
The T-Test operator determines if the null hypothesis (i.e. all actual mean values are the same) holds for the given performance vectors. This operator uses a simple paired t-test to determine the probability that the null hypothesis is wrong. Since a t-test can only be applied on two performance vectors this test will be applied to all possible pairs. The result is a significance matrix.
Paired t-test is a test of the null hypothesis that the difference between two responses measured on the same statistical unit has a mean value of zero. For example, suppose we measure the size of a cancer patient's tumor before and after a treatment. If the treatment is effective, we expect the tumor size for many of the patients to be smaller following the treatment. This is often referred to as the 'paired' or 'repeated measures' t-test.
In case of this operator the dependent samples (or 'paired') t-tests consist of a pair of performance vectors. Doing multiple paired t-tests would result in an increased chance of committing a type I error. 'False positive' or Type I error is defined as the probability that a decision to reject the null hypothesis will be made when it is in fact true and should not have been rejected. It is recommended to apply an additional ANOVA test to determine if the null hypothesis is wrong at all. Please use the ANOVA operator for performing the ANOVA test.
Differentiation
ANOVA
Doing multiple two-sample t-tests would result in an increased chance of committing a type I error. For this reason, ANOVA is useful in comparing two, three, or more means.
Input
performance
This operator expects performance vectors as input and can have multiple inputs. When one input is connected, anotherperformance输入端口可用acc的准备ept another input (if any). The order of inputs remains the same. The performance vector supplied at the firstinputport of this operator is available at the firstperformanceoutput port of the operator.
Output
significance
The given performance vectors are compared and the result of the significance test is delivered through this port.
performance
This operator can have multipleperformanceoutput ports. When one output is connected, anotherperformanceoutput port becomes available which is ready to deliver another output (if any). The order of outputs remains the same. The performance vector delivered at the firstperformanceinput port of this operator is delivered at the firstperformanceoutput port of the operator.
Parameters
Alpha
This parameter specifies the probability threshold which determines if differences are considered as significant. If a test of significance gives a p-value lower than the significance levelalpha, the null hypothesis is rejected. It is important to understand that the null hypothesis can never be proven. A set of data can only reject a null hypothesis or fail to reject it. For example, if comparison of two groups reveals no statistically significant difference between the two, it does not mean that there is no difference in reality. It only means that there is not enough evidence to reject the null hypothesis (in other words, the experiment fails to reject the null hypothesis).