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10. Inferential Data Analysis

As the researcher draws scientific conclusions from his study using only a sample instead of the whole population, he can justify his conclusion with the help of statistical inference tools. The principal concepts involved in statistical inference are the theory of estimation and hypothesis testing.

Theory of Estimation

Point Estimation: A single value is used to provide the best estimate of the parameter of interest.

Interval Estimation: Interval estimates show the estimate of the parameter and also give an idea of the confidence that the researcher has in that estimate. This leads us to the consideration of confidence intervals.

Confidence Interval (CI): A confidence interval estimate of a parameter consists of an interval, along with a probability that the interval contains the unknown parameter. The level of confidence is denoted (1 - α)*100%.

The narrower the width of the confidence interval, the lower is the error of the point estimate it contains. The sample size, sample variance, and the level of confidence all affect the width. Reportings typically require 95% CIs.

Hypothesis Testing

The basic concept used in hypothesis testing is that it is far easier to show that something is false than to prove that it is true. Mutually exclusive hypotheses are created: Null Hypothesis (H0) and Alternative Hypothesis (H1). p-values determine if changes are statistically significant under alpha parameters.

Statistical Tests Parameters Matrix

Variable Type Parameters Tested Variables Count Criteria / Assumption Appropriate Test
Ratio VariableMeanOne>30 (Large Sample)Z-test
Ratio VariableMeanTwo>30 (Large Sample)Z-test for Two Means
Ratio VariableMeanOne<30 (Small Sample)t-test
Ratio VariableMeanTwo<30 (Independent)Independent sample t-test
Ratio VariableMean (Same Subject)Two<30 (Paired)Paired sample t-test
Ratio VariableProportionOne-Binomial
Ratio VariableProportionTwo>30 (Large)Z-test for Proportions
Ratio VariableMeanMore than two>30ANOVA
Ratio VariableMean (Same Subject)More than two>30Repeated measures ANOVA
Nominal/CategoricalAssociationTwo or moreExpected Frequency ≥ 5Chi-square
Ratio VariableMeanTwoNormality Assumption ViolatedMann-Whitney test
Ratio VariableMean (Same Subject)TwoNormality Assumption ViolatedWilcoxon signed rank test
Ratio VariableMeanMore than TwoNormality Assumption ViolatedKruskal Wallis test