Learn to compare 'Complex to understand' scenarios in a nutshell

 

Hello there! We all have gregarious personalities and our friends converse about simple things like comparisons on: healthy people vs unhealthy; fitness vs sedentary; mediocre vs luxury products; frugal person vs society doing well; company sales vs sales men; country population vs global population etc., and we can concur to the fact that, what we usually chat is purely based on theory, assumptions, news and logic without statistical evidences. For that purpose, we have National Statistics departments globally publishing all the necessary data or simply we read off the web. 

To clarify our mundane doubts, we can take help of statistics!

Here, we will use t-Test & z-Test methods to compare your country with a different country where climate, food, population, employment, currency and almost all other things are metaphorical. 

As examples, let us imagine USA vs EU, India vs China or EU vs US vs Asia. 

So we use this model built on t-Test & z-Test formulas:

1.  '((n1-1)*variance 1 Square - (n2-1)*Variance 2 Square)/n1+n2-2'= Eq1.

Therefore, 'SD= Root (Eq1) * Root (1/n1 + 1/n2)' Eq2. 

t= (Mean 1-Mean2)-(SD1-SD2)/Eq2

2.  't= (Mean X (Country A) - SD of sample (Country B))/ (SD (Country A)/Sqrt (10))'

3. 'z=(Mean1-Mean2)-(SD1-SD2)/Sqrt (Variance1/n1)+(Variance2/n2)'

Important thing to remember is that SD1-SD2=0 because we are imagining that both of the countries are same and no difference between them. 

 Use Critical Value Calculator for sample size <30 (t-Value)

Use Critical Value Calculator for sample size >30 (z-Value)

Note: t-Test, z-Test & p-Test is used for Hypothetical testing. The first two finds the differences in averages, while the last one negotiates with null assumptions. n1=31 and n2=50 are the values used for z-Test method.

Now, you can model for fun using your imagination just by changing the names, say, compare shop profits and... :)

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