Engineers use common statistical tools relevant to their experimental data analysis and gain reliable insights for decision-making. They should be used in accordance with the assumptions associated with each method.

✔ Data cleaning ensures that errors, missing values, and outliers are handled appropriately so analyses are based on accurate information.

✔ Descriptive statistics and data visualisation help identify patterns, trends, and anomalies.

✔ Comparative statistics assess the differences between processes, materials or designs, to determine whether observed changes are meaningful or just due to random variations.

✔ Statistical modelling predicts behaviour, optimises processes, and simulates scenarios before implementation. This reduces risk, saves cost, and improves efficiency.

Correct use of these tools enables engineers to make informed, data driven decisions and continuously improve system performance