Subject: Urgent Request for Heating Repair

Dear [Landlord’s Name],

I hope you’re well. I’m writing to follow up about the heating issue at [property address], which I first reported on [date]. It has now been three weeks without working heating, and this needs to be resolved urgently.

I appreciate that repairs can sometimes take time, but the lack of heating is causing real difficulty, especially as the weather is cold. I’m not looking for a dispute — I simply need the heating repaired as soon as possible.

Could you please confirm by [date/time, e.g. tomorrow at 5pm] what steps are being taken and when a qualified engineer will attend? I’m happy to provide access at a convenient time and will do what I can to help the process move quickly.

If the repair cannot be completed immediately, please let me know what temporary solution you can provide in the meantime.

Thank you for your prompt attention to this. I look forward to hearing from you shortly.

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

import { useEffect, useState } from "react";

export function useDebouncedValue<T>(value: T, delayMs: number = 300): T {
  const [debouncedValue, setDebouncedValue] = useState<T>(value);

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

    // Cancels the pending debounce on value/delay change and on unmount.
    return () => {
      clearTimeout(timeoutId);
    };
  }, [value, delayMs]);

  return debouncedValue;
}

Usage:

const debouncedSearch = useDebouncedValue(search, 500);

The cleanup function clears the pending timeout whenever the value changes, the delay changes, or the component unmounts.

Assuming “99% accurate” means:

• 99% of users test positive

• 99% of non-users test negative

then the probability they actually use the drug is about 33%, not 99%.

Using Bayes’ theorem:

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

Plug in the numbers:

[ \frac{0.99 \cdot 0.005} {0.99 \cdot 0.005 + 0.01 \cdot 0.995} = \frac{0.00495}{0.0149} \approx 0.332 ]

So the answer is approximately:

[ \boxed{33\%} ]

A natural-frequency version makes it clearer. Out of 10,000 people:

• 50 use the drug.

  • About 49.5 test positive.

• 9,950 do not use the drug.

  • About 99.5 falsely test positive.

So among the roughly 149 positive tests, only about 50 are true users.

It surprises people because they confuse:

[ P(\text{positive} \mid \text{user}) ]

with

[ P(\text{user} \mid \text{positive}) ]

The test is very accurate, but because drug use is rare, false positives from the much larger non-user population can outnumber true positives. This is called base-rate neglect.