Up to this point you have tested claims about a single population mean. Many engineering questions, however, involve a comparison: does a new alloy have a higher average tensile strength than the current one, does a revised process reduce average cycle time, does a calibration procedure change the average reading of an instrument? Comparing two means is the natural extension of one-sample hypothesis testing, and the mechanics are familiar — state hypotheses about the difference between the means, compute a t statistic, find a p-value, and decide.
The key new decision is which two-sample procedure applies. If the two sets of measurements come from separate, unrelated units, you use an independent samples t-test, which requires attention to whether the two population variances can be treated as equal (pooled) or not (Welch-type). If each measurement in one group is naturally matched to a measurement in the other — the same specimen before and after treatment, the same part measured by two gauges — the data are paired, and you analyze the differences with a one-sample t-test on those differences. Choosing the wrong test misstates the standard error and can lead to the wrong conclusion.
As you watch the videos below, pay attention to three things: how the speaker identifies the design (independent versus paired) from how the data were collected, what assumptions are checked or stated before the test is run, and how the numerical output — test statistic, degrees of freedom, p-value, and confidence interval — is translated back into a statement about the engineering question. Try to reproduce at least one example by hand or in a spreadsheet so the formulas, not just the software output, become familiar.
A short, applied walkthrough of running an independent (two-sample) t-test in SPSS, from setting up the data to reading the output table. Based on its title and description, it covers how to interpret the results SPSS reports, which for this test typically includes a variance-equality check, the t statistic, degrees of freedom, significance value, and a confidence interval for the difference in means.
It gives a compact, concrete picture of what an independent samples t-test produces and how each piece of output is read, which supports the subtopics on the independent samples test, its assumptions, and interpreting results — even if your course uses a different software tool, the output elements are the same.
This tutorial works through a two-tailed paired (repeated measures / matched subjects) t-test starting from raw data, showing how the paired differences are formed and used. The description also indicates it constructs the associated interval estimate, so you see both the test and the confidence interval for the mean difference.
Joshua Emmanuel's statistics tutorials are known for clear, step-by-step numerical work, and this one directly addresses the paired design and the distinction between repeated measures and matched subjects, which is central to selecting the correct test.
A LearnChemE screencast on the paired t-test, with an accompanying spreadsheet, organized around standard engineering textbook material. Expect a worked example in which paired observations are differenced, a t statistic is computed, and the result is compared against a critical value or p-value.
It is produced for engineering students by a university-based channel, so the context and notation are likely to match this course, and the spreadsheet lets you repeat the computation yourself rather than only watching.
Together these videos cover the two core procedures of the module: the independent samples t-test (MO3) with attention to the variance and independence conditions reported in the output (MO2), and the paired t-test presented twice, from raw-data computation and from a spreadsheet, so you can see how matched observations are differenced and why that design is handled differently (MO1, MO3). All three emphasize reading the test statistic, p-value, and confidence interval and stating a conclusion, which supports evaluating results in context (MO5). Note that none of the videos addresses the two-proportion z-test (MO4); for that outcome, rely on the course notes, textbook, and assigned problems.
These videos were chosen from YouTube on October 2, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.