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Why are tests of significance needed?
Tests of significance are statistical tools
that help us make decisions about changes to
responses (process outputs).
Without these tools, we might look at a change
in a process output and think that it is
important, but the change was just part of the
common cause variation from the process.
Tests of significance give us a statistical
basis for determining if a change in factor
levels leads to a statistically significant
effect on the process response.
While tests of significance can be standalone
statistical tools, they serve as the backbone of
ANOVA (analysis of variance) and of the analysis
of the results from designed experiments.
Whenever we make statistics-based decisions,
we have to accept some risk in our assessments.
There are two types of risks we face.
We can make a mistake in saying results are
different when they are actually the same. This
is an α (alpha) risk.
A β (beta) risk occurs when we say that results are
the same when they are actually different.
With tests of significance, a 5% α risk is
- We can place all of the risk on one-tail when
testing for a change in one direction, or we can
divide the risk over two-tails when testing for
any type of difference.
Degrees of Freedom
α risk, there is second term that
we need to use with tests of significance, the
Degrees of Freedom.
The degrees of freedom, or df, are the number
of independent values we have in a calculation.
Typically, this is the number of values
associated with the calculation minus 1.
Hypothesis testing is an important concept
needed for both tests of significance and design
of experiments. A hypothesis is an assumption
about the outcome of the test or experiment.
If a hypothesis is rejected, it means that the
data available are sufficient to conclude that
the hypothesis is false.
However, if the hypothesis is accepted, we can
say that the data are sufficient to conclude
that the hypothesis is not false but not
necessarily that the hypothesis has been proven
Types of Tests of Significance
There are four major types of significance
tests. The Z-test and t-test look at differences
in the mean values and the chi-squared and
F-tests look at differences in variances.
With experimental designs, we use the tests of
significance for samples, the t-test and the
F-test, not the tests for populations.