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Usually, the first step of a statistical analysis is descriptive analysis, where tables, graphs, and simple measures help to quickly assess and summarize important aspects of sample data.

When performing descriptive analysis, take into account the type of variable(s) present, i.e. is it qualitative (categorical) or quantitative? Different graphs, descriptive statistics, and inferential procedures have to be used to study different types of data.

The descriptive phase evaluates the following aspects:

After outlining important sample data characteristics through descriptive statistics, the second step of a statistical analysis is inferential analysis, where sample findings are generalized to the referring population.

We often wish to answer questions about our processes or products to make improvements and predictions, save money and time, and increase customer satisfaction:

 What is the stability of a new formulation?

 What is the performance of a new product compared with the industry standard or products currently on the market?

 What is causing high levels of variation and waste during processing?

These questions are examples of inferential problems.

Inferential problems are usually related to:

Estimation of a population parameter (e.g. a mean)		What is the stability of a new formulation?
Comparison among groups		What is the performance of a new product compared with the industry standard or products currently on the market?
Assessing relationships among variables		What is causing high levels of variation and waste during processing?

We can use several inferential techniques to answer different questions. Later on, we will review the following ones:

Estimation of a population parameter:Point estimateConfidence intervals
Comparison among groups:Hypothesis testing (one‐sample tests; two‐sample tests; ANOVA)
Assessing relationships among variables:Regression models