Businesses run on data, which is both good news and bad news. The good news is that it has never been easier to collect data about your customers, products, and sales. The bad news is that you still have to decide what the data means.
- [Instructor] Let's say that you've performed your analysis. Now it's time to start interpreting it. Businesses run on data, which is both good news and bad news. The good news is that it has never been easier to collect data about your customers, products, and sales. The bad news is that you still have to decide what that data means. As an example, suppose you ran a web advertising campaign with the goal of bringing in at least 70 new qualified customers per week. You ran the ads on 25 different sites for four weeks, so you have a sample size of 100.
On average, each ad brought in 73 new customers with a standard deviation of 15.1. The central question, though, is whether the ads met your goal of bringing in 70 new customers with each campaign. As your sample size is 100 and your mean is 73, the standard deviation is pretty large, over 15. So you have to ask yourself one question. Did I just get lucky? I've used the numbers I collected to calculate my Z-score and a probability. The Z-score lets me calculate how likely it is my results were due to chance.
In this case, that probability is 2.35%. So 2.35%, is that good, bad, neither? Most analysts say that a result is significant if the probability your results were due to chance is below 5%. In other words, if your data shows that your conclusion will be wrong less than one time out of 20, you can claim the result is significant. In this case, 97.65%, or 100% minus 2.35, appears to be significant past the 95% confidence level.
Of course, there's still the possibility that your results actually were due to chance and that your ads did not have a meaningful effect. That's where your knowledge of your business, intuition, and willingness to continue analyzing comes in handy.
- Calculating mean and median values
- Analyzing data using variance and standard deviation
- Working with sample data
- Minimizing errors
- Visualizing data with histograms, charts, and more
- Testing hypotheses
- Measuring covariance and correlation
- Performing Bayesian analysis