Download Introduction to statistical testing: Sign Test

Introduction to statistical
testing: Sign Test
Inferential statistics

We can infer that our results can be generalised to the target population, and as a
consequence is significant.

For example we may do a study and find on average that the men in a study had
better spatial ability than the female subjects, but does that mean that all men
around the world have superior spatial ability? Can we simply generalise the results
from say 1000 participants to all males and females globally?

To see if there is a real difference between the genders, we use statistical tests to
test whether our results from this study could have been due to chance, or if their
really does appear to be a gender difference in spatial abilities.
Introduction to statistical testing
 Differences
chance.
A
between two groups can be caused by
statistical test is calculating the probability that
chance has caused that difference.
Probability recap!

In psychology the significance level is set at p< 0.05, which means that
there is a less than 5% probability that the results occurred by chance.

In this case you are 95% certain that the difference in spatial ability is
down to REAL differences between the genders (manipulation of the IV
caused differences in the DV).
Sign Test
•
Looking at a difference between two conditions
•
Looking at paired or related data generally repeated
measures or matched pairs design
•
Looking at categorical/nominal data
Worked Example
See hand out for example…
AIM: to investigate whether drinking energy
drinks makes people more talkative
Record the data
Step 1: As the sign test uses nominal data we need to convert our data
by working out which participants produced higher word count after
drinking SpeedUpp and which produced a lower word count
For each PP you subtract the score for water from the SpeedUpp score
Record the sign, whether it is positive or negative
AIM: to investigate whether drinking energy drinks
makes people more talkative
Step 2: Count up the number of positive signs
and the number of negative signs.
Step 3: The calculated S value will be the lower
of these two numbers (the less frequent). S=
Step 4: Find the critical value of S so we can
compare it to the observed value of S.
a)
N= the total number of scores (ignoring any
0 or = values)
b)
One-tailed or two-tailed hypothesis
c)
p= 0.05 usual level of probability chosen (1
in 20 likelihood results are due to chance,
5%)
AIM: to investigate whether drinking energy drinks
makes people more talkative
Step 5: compare this to our observed.
the observed value/
calculated value of S ≤ critical value
for the results to be significant
For the Sign test,
(For the data to be significant (and the alternative
hypothesis to be accepted) the calculated value of
S must be EQUAL to or LESS THAN the critical value
identified)
The results are NOT SIGNIFICANT as the calculated
value of S=7 is greater than the critical value=5 (p≤
0.05) for a two tailed test, N=20.
Therefore the NULL hypothesis has to be accepted
and the experimental hypothesis rejected.
Practice will make perfect!
Additional questions:

Complete the exam question
on Pg 71 in OBB – although this
is an independent measures,
treat it as repeated just so you
can practice the sign test

GHG has this example as well
as pg 206-209 in GHG