Significance Testing: This is a statistical method that tests a hypothesis. It's used to determine whether a result is statistically significant, meaning it's unlikely to have occurred by chance. For example, Teton Grand might want to test if there's a significant difference in the average spending of customers who use their loyalty program versus those who don't. They would set up a null hypothesis (e.g., there is no difference in spending) and an alternative hypothesis (e.g., there is a difference in spending). They would then collect data and perform a significance test (like a t-test). If the p-value is less than the chosen significance level (commonly 0.05), they would reject the null hypothesis and conclude that there is a significant difference. Confidence Intervals: These provide a range of values, derived from a data set, that is likely to contain the value of an unknown population parameter. For instance, Teton Grand might want to estimate the average number of visits per customer per year. They could use a sample of their customers to calculate an average and then create a confidence interval around this average. If the 95% confidence interval for the average number of visits is 4 to 6, they can be 95% confident that the true average number of visits for all their customers falls within this range. Standard Error of Measurement: This is a measure of the precision of a test or instrument. It quantifies the amount of error that exists in a measurement. For example, Teton Grand might have a survey to measure customer satisfaction. The standard error of measurement would give them an idea of how much error there is in these measurements. If the standard error is small, it means their survey is a reliable tool for measuring customer satisfaction. If it's large, it means there's a lot of variability in their measurements, which could be due to factors like unclear survey questions or inconsistent responses from customers