Variance - biased or unbiased estimator?

There is a question in the Math for Data Scientist → Central Limit Theorem:
“Based on the resulting mean of the sampling distribution, would you say that variance is a biased or unbiased estimator?”
The answer that is provided in the course is:
"Since the mean of the sampling distribution of the variance is not equal to the variance of the population, it is a biased estimator. However, you may notice that it is close! If we set ddof=1 in the np.var() function, we can calculate sample variance, which is very similar to “population variance” except that the formula has sample_size - 1 in the denominator instead of just sample_size. Sample variance is an unbiased estimator of population variance. "

By my understanding the answer saying both - yes, it’s biased and no, it’s unbiased - at the same time))
Could you explain please, what’s going on here? Is sample variance biased or unbiased estimator?

In the same lesson:

“let’s consider an example from a restaurant serving quarter-pounder burgers. Their quarter-pounders weigh an average of 0.25 lbs with a standard deviation of 0.2 lbs.”

Can a burger have of 0.2? This means once in a while you can get 0.05 lbs burger instead of a quarter-pounder))

Is it a typo?