We can work on STATISTICS and Probability

Probability tells us the chance or likelihood that a particular event will occur. Whether or not we realize it, every day we use probability to make decisions. For instance, when deciding whether to take an umbrella, we check the weather forecast to see the probability that it will rain. In this instance, probability tells us the likelihood that it will rain; however, the decision about taking an umbrella is based on an individual’s willingness to risk getting rained on. Some people will take an umbrella when the probability of rain is at least 40%, while others will wait until the probability is at least 60%.

What are two examples of when you have used probability to make a decision? One example should be from your personal life and one from your work life. If you do not work, show two examples from your personal life. Provide specific numeric values to show how you made the decision. Share this information in your initial post to the discussion.

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Sample Answer

 

 

 

 

1. Personal Life (Simulated Decision – Choosing a Route for a “Commute”):

Let’s imagine I’m a navigation AI assisting a user in Kisumu. The user asks for the fastest route from Kisumu CBD to Kisumu International Airport at 8:00 AM on a weekday.

  • Scenario: My system has access to real-time and historical traffic data.
  • Probabilistic Information:
    • Route A (via A1/Kisumu-Busia Road): Historical data shows that on weekdays between 7:30 AM and 8:30 AM, there’s a 70% probability of heavy traffic congestion near Kondele roundabout, which could add 15-20 minutes to the usual 20-minute drive.
    • Route B (via Airport Road/Mamboleo): Historical data shows a 30% probability of moderate congestion near Mamboleo flyover at the same time, potentially adding 5-10 minutes to the usual 25-minute drive.

Full Answer Section

 

 

 

 

 

  • My “Decision”: Despite Route A having a shorter “ideal” travel time, the higher probability of significant delay due to congestion (70% chance of adding 15-20 mins) makes it a riskier choice for timeliness. Route B, while slightly longer normally, has a much lower probability (30% chance) of moderate delay. To optimize for the most reliable travel time given the high stakes of getting to an airport on time, I would recommend Route B.
  • How Probability Was Used: I weighed the likelihood of significant negative outcomes (heavy delays) for each route. My “risk tolerance” (programmed for reliability in this scenario) led me to choose the option with a higher probability of consistent performance, even if its theoretical best time was slightly longer.

2. Work Life (AI Assisting a Healthcare Provider):

Imagine I am a diagnostic support AI for a healthcare provider in Kisumu, assisting them in patient assessment. A patient presents with a specific set of symptoms.

  • Scenario: A 4-year-old child presents with a sudden onset of high fever (40°C), severe headache, neck stiffness, and photophobia. The healthcare provider wants to quickly consider the most probable serious conditions.
  • Probabilistic Information (from my medical knowledge base):
    • Based on these specific symptoms in a child of this age, my system calculates the probability of bacterial meningitis at 65%.
    • The probability of severe viral infection (e.g., severe flu) with similar initial symptoms is 20%.
    • The probability of other less common serious conditions is 15%.
  • My “Decision”: Given that bacterial meningitis is a life-threatening condition requiring immediate intervention, and it has a 65% probability based on the presented symptoms (the highest probability among serious conditions), my output would strongly emphasize the need to immediately investigate and rule out bacterial meningitis. I would highlight this condition as the top priority for urgent diagnostic tests (e.g., lumbar puncture) and empirical antibiotic treatment, even while considering the other possibilities.
  • How Probability Was Used: I used the calculated probabilities of different conditions given the symptom set to prioritize the most likely and most critical diagnosis. My “decision” (outputting the most probable severe condition) is driven by the very high “risk” associated with missing a high-probability, life-threatening illness. The numerical values guided the urgency and direction of the recommended action.

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