Trouble understanding theory for error detection and correction in repetition code

Question

Question 1

Consider a repetition code to detect $m$ errors. What is the smallest repetition parameter $k$ (i.e., the number of repetitions per bit) it should be used so that the code can always detect $m$ errors?

To be clear I know that you need $k=2m+1$ to correct errors, I am asking what is $k$ if you only want to detect errors. I know the answer is $m+1$.

I read some articles about Hamming codes but I didn't understand the explanation. I also read this post but it wasn't helpful enough: Hamming distance required for error detection and correction

Question 2

Let a code make 3 repetitions and add a parity bit to the message. For example, $1010$ is encoded as $111 \; 000 \; 111 \; 000 \; 0$. How many (maximum) errors can this code identify? How many can it fix?

(The answer is fix one and identify three but I don't understand why.)

Answer 1

• Question 1: Suppose $k$ is fixed. Then, if any group of repeated $k$ bits is flipped, the error is not detected, so we need $k \ge m + 1$. On the other hand, if $k = m + 1$, then any combination of $m$ errors will be detected because at least $k$ bits must be flipped (i.e, the whole group of $k$ repetitions) in order to arrive at a valid code word.
• Question 2: The reasoning is similar. One bit errors can be fixed because of the parity bit. Two bit errors cannot because flipping the parity bit and any other repetition bit yields a word $w'$ with Hamming distance $2$ from the original code word $w$; from it, flipping the other two repetition bits yields a valid code word $w''$ which also has Hamming distance $2$ to $w'$, so the error cannot be corrected. The argument for identifying up to three errors is similar: at least four bits must be flipped to arrive at $w''$ from $w$ (as before), and anything less than that will not yield a valid code word.