Subject: Urgent: Heating Repair Needed

Dear [Landlord’s Name],

I’m writing to follow up about the heating issue at [property address], which I first reported on [date]. The heating has now been out for three weeks, and the problem remains unresolved.

I understand that repairs can sometimes take time, but reliable heating is essential. Please arrange for the system to be repaired as soon as possible and confirm by [reasonable date] when a qualified technician will attend.

I would prefer to resolve this promptly and cooperatively. Please let me know if you need access to the property or any additional information from me to move the repair forward.

Thank you for your prompt attention. I look forward to receiving a confirmed repair date.

Kind regards, [Your Name] [Phone Number] [Property Address]

import { useEffect, useState } from "react";

/**
 * Returns a value that updates only after it has remained unchanged
 * for the specified delay.
 */
export function useDebouncedValue<T>(value: T, delayMs: number): T {
  const [debouncedValue, setDebouncedValue] = useState<T>(value);

  useEffect(() => {
    const timeoutId: ReturnType<typeof setTimeout> = setTimeout(() => {
      setDebouncedValue(value);
    }, delayMs);

    // Cancels the pending update when the value/delay changes
    // or when the component unmounts.
    return () => {
      clearTimeout(timeoutId);
    };
  }, [value, delayMs]);

  return debouncedValue;
}

Example:

function SearchInput(): JSX.Element {
  const [query, setQuery] = useState<string>("");
  const debouncedQuery = useDebouncedValue(query, 300);

  useEffect(() => {
    if (debouncedQuery) {
      // Perform search request.
    }
  }, [debouncedQuery]);

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

Assuming “99% accurate” means:

• 99% sensitivity: users test positive 99% of the time.

• 99% specificity: non-users test negative 99% of the time.

Then by Bayes’ theorem:

[ P(\text{use}\mid +) =\frac{0.99(0.005)} {0.99(0.005)+0.01(0.995)} \approx 0.332 ]

So the probability they use the drug is about 33%.

Why this surprises people

Consider 10,000 people:

• 50 use the drug; about 49.5 test positive.

• 9,950 do not; about 99.5 still test positive falsely.

Thus, among roughly 149 positive tests, only about 49.5 are true positives:

[ 49.5/149 \approx 33\% ]

People often answer 99% because they overlook the very low base rate of drug use. Even a small false-positive rate applied to a large non-user population can produce more false positives than true positives.

Strictly speaking, “99% accurate” is ambiguous; the calculation requires knowing the test’s sensitivity and specificity.