Characteristics of Estimators

You tried to estimate where the donkey's tail should have gone. Your estimates were biased because you did not pin them on the correct spot; they were uniformly too low. However, your estimates did not vary much because they were all close to each other.
Bias refers to whether an estimator tends to either over or underestimate the parameter. In this case, the estimator with the sampling distribution with a mean of 8 is the most biased because it tends to be the most different from the population parameter.
A statistic's sampling variability is usually measured by its standard error; the smaller the standard error, the less the sampling variability. In this case, the sampling distribution with a standard error of 2 has the least sampling variability.
Although this is a fictional parameter, the same thing applies. A statistic is unbiased if the mean of the sampling distribution of the statistic, also known as the expected value, is equal to the parameter. Therefore, an unbiased statistic would have an expected value of 9.

Callback before_footer in local_aigrade component should be migrated to new hook callback for core\hook\output\before_footer_html_generation

line 7225 of /lib/moodlelib.php: call to debugging()
line 7292 of /lib/moodlelib.php: call to {closure}()
line 71 of /lib/classes/hook/output/before_footer_html_generation.php: call to get_plugins_with_function()
line 987 of /lib/classes/output/core_renderer.php: call to core\hook\output\before_footer_html_generation->process_legacy_callbacks()
line 97 of /mod/book/tool/print/index.php: call to core\output\core_renderer->footer()