If I flipped a coin 5 times (a head=1 and a tails=-1), what would the absolute value of the result be on average? I.e., if I flipped five non-absolute values, then averaged them, then got the absolute value as the result, and I did that infinite times, what would be the average value of the result?
What mathematical method would I use to calculate this? I've only taken up to precalculus and I anticipate that calculating this would involve something I haven't learned yet but I don't know what it is.
Edit: I am asking this to understand how I can quantify how much lower sample size rather than an actual shift in the average for which there is a tendency can be expected to cause a deviation.
 A: In addition to the excellent answers by Sextus Empiricus and Dave, when you don't know how to approach a problem, a naive but very effective way to get an approximate answer is by just simulating the process you describe.
In R you could do this as follows:
set.seed(1234)
MC <- 1e5
x  <- numeric(MC)
for(i in 1:MC){
    x[i] <- abs(mean(sample(c(-1, 1), 5, replace = TRUE)))
}
mean(x)

Which results in 0.37564, fairly close to the actual answer. Even when you think you know how to answer a question, simulation can still be useful to 'check' your results.
A: Since this appears to be a self-study question, I will give some guidance rather than solve it all.
You can be very formal about how to define average value (what statisticians and mathematicians would call an expected value), to which the linked content about the law of the unconscious statisticians refers. However, taking advanced proofs as given (they are, after all, true), the average you seek is the sum of the values resulting from your procedure (add up the values, then take the absolute value), with each value weighted by the probability of it occurring.
Define $v_i$ as the value arising from applying your procedure to flipping $i$ heads, so $v_5 = 5$, $v_0=5$, $v_2 = 1$, etc. Next, define $p_i$ as the probability of flipping $i$ heads, so $p_5 = p_0 = 0.5^5 = 0.03125$, etc.
Then the sum of the values weighted by the probability of obtaining that value, is:
$$
\overset{5}{\underset{i=0}{\sum}} p_iv_i = p_0v_0 + p_1v_1 + p_2v_2+p_3v_3 + p_4v_4 + p_5v_5
$$
Now just calculate those $p_i$ and $v_i$ values, and I've already given you some (but make sure you know how to calculate them). I think you will find $v_i$ trivial. For $p_i$, you will find life easier if you use the PMF of the binomial distribution, which is contained in the linked Wikipedia article in the comments (and again here).
A: There are 32 different equally probable outcomes of 5 throws, of which two have absolute value of sum of throws equal to 5, 10 have 3, and 20 have 1, so total sum of all these cases is $5\times2 + 3\times10 + 1\times20 = 60$. To get average, we need to devide 60 by number of total cases which is 32. $\frac{60}{32} = \frac{15}{8}$. That is expectation of sums, average is sum divided by 5, so if you want to get expectation of averages, then you divide expectation of sums by 5 and get $\frac{3}{8}$.
A: The distribution is binomial distributed and with that you can compute this manually.
If $X$ is the average of five coin flips (which I assume are fair) then
$$\begin{array}{}
P(X = -1) &=& \frac{1}{2^5}\\
P(X = -0.6) &=& \frac{5}{2^5}\\
P(X = -0.2) &=& \frac{10}{2^5}\\
P(X = 0.2)& =& \frac{10}{2^5}\\
P(X = 0.6) &=& \frac{5}{2^5}\\
P(X = 1) &=& \frac{1}{2^5}
\end{array}$$
And the expectation value for any function of the variable is
$$E[f(X)] = \sum_x P(X=x) \cdot f(x)$$
with $f(X) = |X|$ you can get your answer which should be close to the approximation below.

In the limit, you can estimate this with the half normal distribution, which has an expectation value of $\sigma \sqrt{\frac{2}{\pi}}$ and the distribution of the mean of $n$ coin flips is approximated by taking $\sigma = 1/ \sqrt{ n}$ giving
$$E[|X_n|] \approx \sqrt{\frac{2}{n\pi}}$$
which is equal to about $0.3568248$ in the case of $n = 5$, a bit less than 5% away from the exact answer.
