Introduction to Inferential Statistics

Plain Data 3

Inferential Statistics

Introduction

One of the primary concerns that everyone probably has experienced, would be the certainty level of a result driven from analysis of a sample size, out of a population.

Inferential statistics simply can be defined as mean, consisting of multiple tools which helps drawing conclusions about a population, through examination and assessment of a given or available random samples. In another word, Inferential statistics supports reaching to generalizations about a population, by a set of tests and further analytical methods performed on a sample size, gleaned from the vey same population.

There are two major types of inferential statistics:

• Hypothesis Testing

Usually is initiated by setting up a null hypothesis and an alternative hypothesis, followed by conducting a statistical test of significance.

F-test, z-test, t-test and ANOVA are common examples of hypothesis testing. Each will be elaborated in upcoming chapters. 

Intuitive example explaining the basic idea underlying an one-sample hypothesis test

• Regression

Regression analysis is implied when the objective is to quantify the alteration of a variable with certain precision, with respect to another variable. Most widely used Regression is the linear regression, while there are other types, such as multiple linear, nominal, logistic and ordinal regression.

Linear regression, measures how a dependent variable will react to a change of units of an independent variable. 

Example:

Assume that the mean of IQ scores of 200 students are known in a country with more than 25 million habitants. Inferential statistics can be useful to estimate the mean IQ score of the total students, country-wide. 

Conclusion

So far, the inferential statistics was briefly introduced, but one important point remains to be mentioned; The sampling need to comply with certain requirements, which will make the samples random, while representing the population with high accuracy. So sampling technique is vital for inferential statistics to be functional.  

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