A statistical test lets you make quantitative decisions about the research process. The main aim of performing experiments is to find a significant impact between two stimuli that are being tested. Depending on the nature of a study, statistical tests can be of various types. In the field of psychology, the most commonly used statistical tests include ANOVA, t-test, chi-square test, z-test, f-test, etc. These tests are used to determine the significance between the hypothetical or expected samples or observed samples. For example, if a researcher wants to perform a statistical test to find out the difference between the EQ levels of two employees, then the researcher can conduct the t-test for the difference of the two samples. If one wants to test the goodness of fit of a particular model, then he/she can use the chi-square test.
In a psychology experiment, dependent and independent variables are the stimuli that are being manipulated and behavior being measured and is accomplished via statistical tests. While both ANOVA and t-test are popular and are widely used, most often research scholars go for ANOVA test over t-test to confirm if the behavior occurring is more than once. This is because t-test compares the means between the two samples; but if there are more than two conditions in an experiment an ANOVA test is required. The ANOVA test can evaluate more than one treatment and this is the major advantage over t-test and also opens up several testing capabilities.
ANOVA also enables the researcher to see how effective two different kinds of treatment are and how durable they are. ANOVA test can provide information about how well a treatment works and how long it lasts.
Although it is easy to perform t-test, there are several issues one face while using this test. The more hypothesis test a researcher uses, the more risk of having type I error and also has less power. Whereas ANOVA lets you test more than two means without paving the way to any errors.
To conclude, the ANOVA test is much needed especially when the study design has two or more conditions to be compared. While it is less daunting and simple to conduct a t-test, but the risk of making a type I error is higher in this test, which can ruin your experiments. Hence, it becomes a mandate to opt for the right statistical test and save your research from sinking.
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