Hypotheseis Tests and Nominal Distributions

Hypothesis tests with normal distributions are often used when data can be modelled by a symmetric, continuous distribution.
Normal distribution is represented by two parameters: the mean (µ) and the standard deviation (σ).
These parameters are key features in hypothesis testing.
Null hypothesis proposes a specific value for the population mean or proportion.
Alternative hypothesis suggests the actual value may be greater, lesser, or different than proposed.
A test statistic (z-score or t-score) is calculated from sample data.
This statistic, the critical region, significance level, or p-value is used to accept or reject null hypothesis.
Mastery of these tests is important for analysing continuous data in many disciplines.