Running a hypothesis test is only half the job. The other half — and the part that engineers are judged on in reports, design reviews, and publications — is explaining what the result actually means. This topic focuses on the chain of reasoning that connects a test statistic to a p-value, a p-value to a decision at a chosen significance level, and that decision to a clear, honest statement about the engineering question you started with.
Along the way you will look closely at what a p-value is and is not. It is the probability of observing data at least as extreme as yours assuming the null hypothesis is true; it is not the probability that the null hypothesis is true, not the probability that you made a mistake, and not a measure of how large or important an effect is. Confidence intervals help here: they carry the same information as the test but express it on the scale of the quantity you care about, which makes it much easier to separate statistical significance from practical significance.
As you watch the videos below, pay attention to three things: how the test statistic is computed and what it measures in standard-error units, exactly how the p-value is read off a z-table or software output and compared to alpha, and the precise wording used for conclusions ("reject" versus "fail to reject," and never "prove"). Notice also any cautions the presenters give about common misreadings, since those are the errors that most often show up in written reports.
A compact worked walkthrough of a hypothesis test for a population mean when the population standard deviation is known, using the p-value approach. It shows how the z test statistic is computed from the sample mean, how the corresponding tail probability is found in a z-table, and how that p-value is compared against the significance level to reach a decision. Multiple examples illustrate the mechanics for different alternative hypotheses.
Joshua Emmanuel's videos are widely used in engineering statistics courses for their clean, step-by-step numerical presentation, and this one directly supports the subtopics on understanding the test statistic and on significance levels as decision thresholds.
Part of a short introductory series on hypothesis testing, this video lays out the standard sequence of steps in a test and shows where the p-value fits within that sequence. It connects the null and alternative hypotheses, the test statistic, and the resulting p-value to the final accept-or-reject decision. The earlier videos in the series on hypotheses and on Type I and Type II errors are linked for context.
Because it frames the p-value inside a complete, ordered procedure, it is a good reference for structuring and reporting your own test results clearly rather than treating the p-value as an isolated number.
An interpretation-focused explanation of what a p-value actually represents and why the 0.05 threshold became conventional in scientific practice. The video discusses how researchers use p-values to decide whether an observed result is worth taking seriously, with attention to the reasoning behind the number rather than its computation.
This is the best fit for the subtopics on interpreting p-values correctly and avoiding common misinterpretations, since it emphasizes meaning and context instead of formula mechanics.
These videos reinforce the interpretation side of the module, especially MO4. The first provides the computational bridge from a test statistic to a p-value, supporting MO2 by making clear what the statistic measures and how its magnitude drives the resulting probability. The second places that calculation inside a full testing procedure, which helps when you must justify your choice of test and assumptions under MO1 and MO3 and then write up the result. The third concentrates on correct interpretation and the limits of the 0.05 convention, which is essential groundwork for distinguishing statistical from practical significance and for recognizing when a significant p-value does not imply an engineering-relevant effect. Together they prepare you to report two-sample and paired-sample results accurately, and to recognize when a result's fragility signals the need for the alternative approaches discussed in MO5.
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.