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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