Distributions

In this lesson

1. 0:00 Normal distributions and the bell curve
2. 3:01 Standard deviation and data spread
3. 6:49 Calculating Z-scores explained
4. 9:16 Using Z-scores with normal distribution tables
5. 13:43 Z-score analysis worked examples
6. 19:02 Complete problem solving from scratch
7. 23:05 Business applications and limitations

About this lesson

Normal distributions appear frequently in real-world scenarios—from product weights to population measurements—and follow a recognisable bell curve pattern where data clusters around a mean with predictable spread. You'll discover how standard deviation measures data spread and how the normal distribution divides into predictable ranges: approximately 68% of values fall within one standard deviation of the mean, 95% within two, and 99.7% within three. This foundation lets you calculate z-scores—a standardised measure showing how many standard deviations any value sits above or below the mean—and use normal distribution tables to find corresponding percentages. The lesson walks you through the z-score formula (value minus mean, divided by standard deviation) and demonstrates how to read standard normal distribution tables to extract probabilities. You'll see practical applications: a food manufacturer using z-scores to check whether product weights stay within acceptable ranges, or determining what proportion of a population falls above or below a specific threshold. By the end, you'll be confident interpreting z-scores—both positive and negative—and translating table percentages into business insights. This skill is essential for management accounting roles where you need to assess risk, quality control, and data-driven decisions across any industry.

Duration: 26m