Problems always emerge in every situation and it is the role of engineers to solve them based on a scientific approach. Generally, this approach starts from creating a hypothesis, then conducting experiments, and lastly drawing conclusions.
In the context of Industrial Engineering, collecting, analyzing, and interpreting data is one of the core activities during the experiment phase and this is when Statistics comes into play. According to the book Applied Statistics and Probability for Engineers (Montgomery & Runger, 2003): “The field of statistics deals with the collection, presentation, analysis, and use of data to make decisions, solve problems, and design products and processes”.
Having knowledge in statistics helps engineers understand the existing system and provides intuition on how to improve them. Moreover, this book also states that statistics aids us in understanding variability, successive system observations with different results, and its sources. For example, it is impossible for 1 liter of gasoline in a car tank to reach the exact same distance every time.
Two methods of statistical methods are very common for data analysis: Descriptive Statistics and Inference Statistics. Descriptive Statistics compiles the quantitative feature of the data and summarizes the data characteristics. The information from descriptive statistics solely comes from the considered data and any knowledge gained outside the data is disregarded.
Some characteristics example for descriptive statistics including mean, median, variance, skewness, kurtosis, skewness, Scatter Plot, Boxplot, and many others. On the other hand, Inference Statistics conducts tests to determine the data distribution and deriving estimates from the given sample data.
Inference Statistics answers questions such as how significant the difference between two data samples is, whether the population of these two data samples has the same distribution and many others. Statistical Inference applies probability theory to draws conclusions about data population and model estimation. Example of statistical inference tests includes ANOVA Test, t-Test, linear regressions, etc.
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