The Normal Distribution and Z-scores

Hypothesis testing is the process of determining the statistical significance of a proposed population mean in comparison to a sample mean.  The probability result is compared to a level of significance (typically .05) that indicates the degree to which the results of the test are due to chance.  

The most common foundation for hypothesis testing is the normal distribution.  The normal distribution assumes that a sample distribution can be represented by a bell curve and that this distribution will be representative of the population.  With that mind, we can use the assumption of normality to determine probability values for any data point in the sample by converting any raw score to a z-value.  In the following video, you will learn about the normal distribution and how z-scores are calculated and used.