Type 1 and Type 2 errors are confusing. In 3 minutes, I'll demolish your confusion. Let's dive in.
1. Type 1 Error (False Positive): This occurs when the pregnancy test tells Tom, the man, that he is pregnant. Obviously, Tom cannot be pregnant, so this result is a false alarm. In statistical terms, it's detecting an effect (in this case, pregnancy) when it actually doesn't exist.
2. Type 2 Error (False Negative): This happens when Lisa, who is actually pregnant, takes the test, and it tells her that she's not pregnant. The test failed to detect the real condition of pregnancy. In statistical terms, it's failing to detect a real effect (pregnancy) that is there.
3. Cost of Type 1 Error (False Positive): Telling someone who cannot be pregnant (like a man) that they are pregnant could lead to unnecessary stress, confusion, and possibly unnecessary medical consultations. While this is certainly costly in terms of emotional distress, it's largely a temporary situation that can be clarified with further testing.
4. Cost of Type 2 Error (False Negative): Telling someone who is actually pregnant that they are not pregnant could have more significant and longer-term consequences. For the pregnant individual, this might mean a delay in receiving prenatal care, which is crucial for the health of both the mother and the baby.
5. Business Context (Type 2 error cost > Type 1 error cost): In many business contexts, Type 2 errors (false negatives) are considered more critical than Type 1 errors (false positives) just like in the Pregnancy situation. Let's dive in.
6. Risk Management: In risk management, a Type 2 error might mean failing to detect a real risk or threat to the business, such as fraud, market shifts, or financial instability. This oversight can lead to significant financial losses, reputational damage, or even the failure of the business if the risk materializes and it is too late to mitigate it effectively.
7. Customer Retention: In customer-focused industries, not detecting shifts in customer preferences, satisfaction levels, or emerging needs can result in a decline in customer loyalty and market share. A Type 2 error in this context means missing the warning signs until it's too late to address the underlying issues effectively.
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Type 1 and 2 error is great. But there’s a lot more to applying data science to business.
I'd like to help. Here's how:
👉 My Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f
👉 Learn ChatGPT for 10X Faster DS Projects: I have a live workshop where I'll share how to use ChatGPT for Data Science (so you can complete projects 10X faster): https://lnkd.in/gCvh6UAy
If you like this post, please reshare ♻️ it so others can get value (follow me, 🔥 Matt Dancho 🔥 for more data science concepts).
1. Type 1 Error (False Positive): This occurs when the pregnancy test tells Tom, the man, that he is pregnant. Obviously, Tom cannot be pregnant, so this result is a false alarm. In statistical terms, it's detecting an effect (in this case, pregnancy) when it actually doesn't exist.
2. Type 2 Error (False Negative): This happens when Lisa, who is actually pregnant, takes the test, and it tells her that she's not pregnant. The test failed to detect the real condition of pregnancy. In statistical terms, it's failing to detect a real effect (pregnancy) that is there.
3. Cost of Type 1 Error (False Positive): Telling someone who cannot be pregnant (like a man) that they are pregnant could lead to unnecessary stress, confusion, and possibly unnecessary medical consultations. While this is certainly costly in terms of emotional distress, it's largely a temporary situation that can be clarified with further testing.
4. Cost of Type 2 Error (False Negative): Telling someone who is actually pregnant that they are not pregnant could have more significant and longer-term consequences. For the pregnant individual, this might mean a delay in receiving prenatal care, which is crucial for the health of both the mother and the baby.
5. Business Context (Type 2 error cost > Type 1 error cost): In many business contexts, Type 2 errors (false negatives) are considered more critical than Type 1 errors (false positives) just like in the Pregnancy situation. Let's dive in.
6. Risk Management: In risk management, a Type 2 error might mean failing to detect a real risk or threat to the business, such as fraud, market shifts, or financial instability. This oversight can lead to significant financial losses, reputational damage, or even the failure of the business if the risk materializes and it is too late to mitigate it effectively.
7. Customer Retention: In customer-focused industries, not detecting shifts in customer preferences, satisfaction levels, or emerging needs can result in a decline in customer loyalty and market share. A Type 2 error in this context means missing the warning signs until it's too late to address the underlying issues effectively.
===
Type 1 and 2 error is great. But there’s a lot more to applying data science to business.
I'd like to help. Here's how:
👉 My Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f
👉 Learn ChatGPT for 10X Faster DS Projects: I have a live workshop where I'll share how to use ChatGPT for Data Science (so you can complete projects 10X faster): https://lnkd.in/gCvh6UAy
If you like this post, please reshare ♻️ it so others can get value (follow me, 🔥 Matt Dancho 🔥 for more data science concepts).
