Subject: Heating Repair — Follow-Up After Three Weeks

Dear [Landlord’s Name],

I hope you’re doing well. I’m writing to follow up on the heating issue in [Unit/Address], which has now been outstanding for three weeks. I know things can get busy, but with the colder weather setting in, the unit is becoming difficult to live in comfortably.

Could you please confirm when a repair can be scheduled? I’m happy to provide access at a time that works for you or the technician—just let me know what works best.

If there’s anything you need from me to move this forward (approval for a specific contractor, etc.), please don’t hesitate to ask. I’d really appreciate it if we could get this resolved by [specific day, e.g., the end of this week].

Thank you for your attention to this. I’m sure we can get it sorted quickly.

Warm regards,

[Your Name] [Your Address/Unit Number] [Phone Number] [Email Address]

Here is a typed useDebounce hook. It stores the debounced value in state and clears the pending timeout in the useEffect cleanup—so it cancels automatically on unmount.

import { useState, useEffect } from 'react';

export function useDebounce<T>(value: T, delay: number): T {
  const [debouncedValue, setDebouncedValue] = useState<T>(value);

  useEffect(() => {
    const timer = setTimeout(() => {
      setDebouncedValue(value);
    }, delay);

    // Cancel the pending update on unmount or when value/delay changes
    return () => {
      clearTimeout(timer);
    };
  }, [value, delay]);

  return debouncedValue;
}

Usage example:

function SearchInput() {
  const [text, setText] = useState('');
  const debouncedText = useDebounce(text, 300);

  // debouncedText updates 300ms after the user stops typing
  // and the timer is cleared if the component unmounts
}

Assume “99% accurate” means:

• Sensitivity = 99% (true positive rate)

• Specificity = 99% (true negative rate)

• Prevalence = 0.5% = 0.005

Use Bayes’ theorem (or a concrete population):

Total positives = 495 + 995 = 1,490

[ P(\text{use} \mid +) = \frac{495}{1490} \approx \frac{0.99 \times 0.005}{0.99 \times 0.005 + 0.01 \times 0.995} \approx \mathbf{33.2\%} ]

So even with a “99% accurate” test, the chance is only about 1 in 3.

Why it surprises people

This is the base rate fallacy. Our intuition anchors on the test’s 99% accuracy and expects ~99% confidence. But because drug use is rare (0.5%), the pool of healthy people is enormous—so the 1% false-positive rate generates far more false alarms (995) than true detections (495). The false positives swamp the true positives, dragging the posterior probability way down.

Key takeaway: When a condition is rare, a positive result on an imperfect test is more likely to be a false alarm than a true hit—no matter how “accurate” the test sounds.