We have an application where users enter prices all day. These prices are recorded in a table with a timestamp and then used for producing charts of how the price has moved. Every now and then, the user enters a price wrongly (e.g. puts in a zero too many or too few) which somewhat ruins the chart (you get big spikes). We’ve even put in an extra confirmation dialog if the price moves by more than 20%, but this doesn’t stop them entering wrong values.
What statistical method can I use to analyse the values before I chart them to exclude any values that are way different from the rest?
EDIT: To add some meat to the bone. Say the prices are share prices (they are not but they behave in the same way). You could see prices moving significantly up or down during the day. On an average day, we record about 150 prices and sometimes one or two are way wrong. Other times, they are all good.
Calculate and track the standard deviation for a while. After you have a decent backlog, you can disregard the outliers by seeing how many standard deviations away they are from the mean. Even better, if you’ve got the time, you could use the info to do some naive Bayesian classification.