One way to work this out is to look at what happens for, say, Key Stage 5 1000 transactions Testing, Testing 1% of the transactions are fraudulent so we would expect: 10 fraudulent transactions and 990 normal transactions Student worksheet If we just look at the fraudulent transactions it would detect 9 of the 10 fraudulent transactions and would have one false negative. Credit Card Fraud If we look at the normal transactions it would correctly identify A computer program that looks at credit card transactions has a 90% of them as negative i.e normal (90%x990=891) and 10% 90% chance of correctly identifying credit card fraud and a 90% (incorrectly) as fraudulent (10%x990=99). We can add all these chance of correctly identifying normal transactions. figures to the table below. If detecting fraud is a POSITIVE result this table shows the four Fraud Test shows Fraud Test shows Total possible outcomes. positive negative Normal transaction 99 891 990 Fraud Test Shows positive Fraud Test shows negative Fraudulent Transaction 9 1 10 Normal FALSE POSITIVE Correct answer transaction Test shows fraud when it The test does not detect So just looking at the first column the computer would flag up a is a normal transaction fraudulent behaviour and there isn’t any total of 108 cases of fraud, but only 9 of them (or approximately Fraudulent Correct answer FALSE NEGATIVE 8% would be correct). So most of the time when credit card Transaction The test correctly The test does not show fraud identifies fraud when there is fraud is detected it hasn’t really happened. SPAM! SPAM! SPAM! SPAM! If 1% of all credit card transactions are fraudulent, how likely is it A spam filter has a 90% chance of correctly identifying SPAM that when the computer detects fraud that it has actually emails and a 1% chance of identifying a ‘proper’ email as SPAM. happened? If 20% of all email is SPAM and a company receives 10,000 At first glance it looks like 90% of transactions flagged by the emails a day then complete the table below: computer as fraudulent would actually be fraud, but this is far SPAM filter blocks email SPAM filter lets Total from the truth.
Key Stage 5 Testing, Testing Student worksheet Credit Card Fraud A computer program that looks at credit card transactions has a 90% chance of correctly identifying credit card fraud and a 90% chance of correctly identifying normal transactions. If detecting fraud is a POSITIVE result this table shows the four possible outcomes. Fraud Test Shows positive Fraud Test shows negative Normal transaction FALSE POSITIVE Test shows fraud when it is a normal transaction Correct answer The test does not detect fraudulent behaviour and there isn’t any Fraudulent Transaction Correct answer The test correctly identifies fraud FALSE NEGATIVE The test does not show fraud when there is If 1% of all credit card transactions are fraudulent, how likely is it that when the computer detects fraud that it has actually happened? At first glance it looks like 90% of transactions flagged by the computer as fraudulent would actually be fraud, but this is far from the truth. One way to work this out is to look at what happens for, say, 1000 transactions 1% of the transactions are fraudulent so we would expect: 10 fraudulent transactions and 990 normal transactions If we just look at the fraudulent transactions it would detect 9 of the 10 fraudulent transactions and would have one false negative. If we look at the normal transactions it would correctly identify 90% of them as negative i.e normal (90%x990=891) and 10% (incorrectly) as fraudulent (10%x990=99). We can add all these figures to the table below. Fraud Test shows positive Fraud Test shows negative Total Normal transaction 99 891 990 Fraudulent Transaction 9 1 10 So just looking at the first column the computer would flag up a total of 108 cases of fraud, but only 9 of them (or approximately 8% would be correct). So most of the time when credit card fraud is detected it hasn’t really happened. SPAM! SPAM! SPAM! SPAM! A spam filter has a 90% chance of correctly identifying SPAM emails and a 1% chance of identifying a ‘proper’ email as SPAM. If 20% of all email is SPAM and a company receives 10,000 emails a day then complete the table below: SPAM filter blocks email SPAM filter lets Total