Hypothesis Testing is as easy as 1,2,3 (#41)
Hypothesis Testing is a statistical tool used to
determine a relationship between one variable and another. This blog includes an
example of a Hypothesis test in a clear, definable guide.
Tire Pressure vs Tread Wear
Define the Variable Inquiry:
-Does raising the tire pressure directly
result in increased tread wear?
Define “X”
-X Equals Tire Pressure
Define “Y”
-Y Equals Tread Wear
**Y is the variable that is being measured
Perform Analysis Testing
-Collect Data
-Compute Pearson Correlation Co-efficient and
P-Value
Perform Correlation Analysis
-Use Pearson values
-Ex: Pearson(r) = 0.554 with a P-Value =
0.0228
From these results we can conclude a medium relationship
between the two variables. Also, the included data is significant to the
results at the Alpha risk level of 0.0228.
Here is what the “R” Values mean:
-A value in-between positive 0.2 and negative
0.2 could cause a random chance of a relationship
-A value in-between positive 0.8 and 1.0 as well
as negative 0.8 and 1.0 is cause for a strong chance of a relationship
Any value found outside of the previously
mentioned boundaries would reveal a weak to moderate relationship between the
two variables.
Correlation is NOT causation! Correlation
Analysis assesses how strong the relationship is between variables. The R Value
reveals how strong the relationship is between the two variables and the P
Value reveals how significant that relationship is.
Hopefully this provides a clearer view into
the value of Hypothesis Testing. Comment with some of your own experiences with
Hypothesis Testing!
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