Why use ANOVA over T-Test for Conducting Statistical Test in Psychology?

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Why use ANOVA over T-Test for Conducting
Statistical Test in Psychology?
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.
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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.
This Blog is Originally Issued Post by - Puneet
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