Paired T-Test test is used to compare the means of two
sets of observations where the comparison happen
between eachrow of data.
Each row of data in both the column are comparable
to each other
Data Requirements For Paired T-Test
The data from each sample must be in separate
numeric columns of equal length. Each row
contains the pairedmeasurements for an observation.
If either measurement in a row is missing,
Minitab automatically omits that row from the
calculations.
Pre-requisite for Paired T-Test
Your dependent variable should be measured at a
continuous level and be approximately normally
distributed
Your independent variable should consist of two
categorical,"relatedgroups" or "matched pairs".
You should have independence of observations, which
means that there is no relationship between the
observations in each group or between the groups
themselves.
There should be no significant outliers.
There needs to be homogeneity of variances.
Pre-requisite for Paired T-Test
An Automation Engineer claiming to have reduced
First Response Time across ticket categories using
his RPA solutions. The team lead collects the data,
for 10 ticket categories. At 95% confidence
level help team lead validate if the claim made is
valid.
Let us conduct paired T test to find out if
the difference is significant enough
Step 1.a: Conduct Normality test
Note: You can also evaluate the normality test by selecting
Minitab -> Stats -> Basic Statistics -> Normality Tests
(or)
Minitab -> Graph -> Probability Plot
Step 1.b:Normality Check
Interpret:
As P-value is
greater than 0.05,
we can conclude
that the data are
normal and doesn’t
have any outliers.
Interpret:
As P-value is
greater than 0.05,
we can conclude
that the data are
normal and doesn’t
have any outliers.
Step 2: Hypothesis
Null Hypothesis Ho: There is no significant reduction in
first response time across ticket categories after using RPA
solutions
μ(First response time before RPA solutions – First response
time after RPA solutions)= 0
Can be rewritten as i.e. μ_Difference = 0
Alternate Hypothesis Ha: There is significant reduction in
first response time across ticket categories after using
RPA solutions
μ(First response time before RPA solutions –
First response time after RPA solutions) > 0
Can be rewritten as i.e. μ_Difference > 0
Note: μ is the mean of first response time before RPA
solutions and after RPA solutions)
Step 3: Conduct Paired T-Test
Step 4: Interpretation
At 95% confidence level, the lower bound for difference in
the mean would be 1.464
The Lower bound difference in mean (1.464) is greater than
the hypothesized difference (0), which is in line with the
stated Alternate Hypothesis & hence reject NullHypothesis
P-value (0.000) less than alpha (0.05), also indicating to
reject the null hypothesis.
With the above two justification we can conclude there is a
significant reduction in first response time after
using RPA solutions