'Significance' in statistics means 'low probability of obtaining results at least as extreme as actually obtained'. You need to make some assumptions before you can proceed. Here is one example:
You can require that in a valid course a certain fraction of students needs to pass, say 90%. You can also fix the so-called ’significance level’ $\alpha$ to some low probability value, say 5%. In addition, you can make the assumption that between repeated courses the number of students that pass is distributed according to the binomial distribution with the above required probability $p$ (90% = 0.9). I.e. you model passing of a student by an flip of an 'unfair' coin.
You can then use the CDF of the binomial distribution to show that, for a class of 4 students, the probability of only two (or less) of them passing is greater than 5%, even though each student has individually a 90% chance of passing.
In Python (this code is adopted from GeeksforGeeks):
from scipy.stats import binom
# setting the values
# of n and p
n = 4
p = 0.9
# defining the list of k values
k_values = list(range(n + 1))
# obtaining the mean and variance
mean, var = binom.stats(n, p)
# list of CDF values
dist = [binom.cdf(k, n, p) for k in k_values ]
# printing the table
print("k\tCDF(k)")
for i in range(n + 1):
print(str(k_values[i]) + "\t" + str(dist[i]))
# printing mean and variance
print("mean = "+str(mean))
print("variance = "+str(var))
results in:
k CDF(k)
0 9.999999999999991e-05
1 0.003699999999999998
2 0.05229999999999998
3 0.34389999999999993
4 1.0
mean = 3.6
variance = 0.35999999999999993
