From the course: Statistics Foundations 3: Using Data Sets
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Type one and type two errors
From the course: Statistics Foundations 3: Using Data Sets
Type one and type two errors
- In our hypothesis tests, we've always set up a null hypothesis and an alternative hypothesis. The null hypothesis typically assumes the status quo prevails. The null hypothesis might state that the system works or it might tell us that nothing has changed in our system. Our alternative hypothesis assumes the opposite. The alternative hypothesis might tell us that the system is broken. It might tell us that things have changed. Let's use a special type of cancer screening test as an example. This fictional screening would provide a reading based on your blood. The average reading is 100. People that get a reading over 125 get a positive test result. This would indicate they have cancer. If we're going to equate this to a hypothesis test, we would say the cancer screening had two hypotheses. The null hypothesis would state that everything's okay. The person being tested does not have cancer. The alternative hypothesis…
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