Subject: Heating Repair – Request for Urgent Update

Dear [Landlord’s Name],

I hope you’re well. I’m writing to follow up regarding the heating issue, which has now been outstanding for three weeks.

I understand that repairs can take time, but the lack of heating is becoming increasingly difficult, especially as the weather is getting colder. I would be grateful if you could confirm when the repair is scheduled and what steps are being taken to resolve the issue.

If there is anything you need from me to allow access or make this easier, please let me know. I would really appreciate a prompt update and a clear timeline so we can get the heating restored as soon as possible.

Thank you for your help, and I look forward to hearing from you.

Best regards, [Your Name]

import { useEffect, useState } from "react";

/**
 * Debounces a changing value.
 *
 * - Returns `value` before `delayMs` has elapsed.
 * - Updates to the latest `value` after `delayMs` has passed since the last change.
 * - Cancels the pending timeout when `value` changes or when the component unmounts.
 */
export function useDebouncedValue<T>(value: T, delayMs: number): T {
  const [debouncedValue, setDebouncedValue] = useState<T>(value);

  useEffect(() => {
    const timeout = setTimeout(() => {
      setDebouncedValue(value);
    }, delayMs);

    return () => {
      // Cancels the pending debounced update on:
      // 1. unmount
      // 2. value/delayMs changes
      clearTimeout(timeout);
    };
  }, [value, delayMs]);

  return debouncedValue;
}

export default useDebouncedValue;

Usage:

import { useDebouncedValue } from "./useDebouncedValue";

function SearchInput() {
  const [query, setQuery] = useState("");
  const debouncedQuery = useDebouncedValue(query, 300);

  return <input value={query} onChange={(e) => setQuery(e.target.value)} />;
}

Assuming “99% accurate” means:

• If someone uses the drug, the test is positive 99% of the time.

• If someone does not use the drug, the test is negative 99% of the time, so it gives a false positive 1% of the time.

Then by Bayes’ theorem:

[ P(\text{uses} \mid \text{positive}) = \frac{P(\text{positive} \mid \text{uses})P(\text{uses})} {P(\text{positive} \mid \text{uses})P(\text{uses}) + P(\text{positive} \mid \text{non-user})P(\text{non-user})} ]

[ = \frac{0.99 \times 0.005}{0.99 \times 0.005 + 0.01 \times 0.995} ]

[ = \frac{0.00495}{0.00495 + 0.00995} = \frac{0.00495}{0.0149} \approx 0.332 ]

So the probability is about 33%.

Why this surprises people

People focus on the test being “99% accurate” and think a positive result probably means the person uses the drug. But most people are not users.

Imagine 100,000 people are tested:

• 500 are users

  • True positives: (500 \times 0.99 = 495)

• 99,500 are non-users

  • False positives: (99,500 \times 0.01 = 995)

Total positives:

[ 495 + 995 = 1,490 ]

Of those who test positive, only 495 are actual users:

[ \frac{495}{1490} \approx 33\% ]

Even a 1% false-positive rate creates more false positives than true positives because the base rate of drug use is very low. This is called the base rate fallacy.