, which is a function of the data, to approximate the true value of
The final pillar of our lecture is hypothesis testing. This is the formal procedure for deciding between two competing claims: the null hypothesis and the alternative hypothesis. We use a test statistic to determine if the observed data is sufficiently extreme to warrant rejecting the null hypothesis. This process involves a delicate balance between Type I errors (false positives) and Type II errors (false negatives). The p-value, perhaps the most famous metric in statistics, tells us the probability of obtaining results at least as extreme as the ones observed, assuming the null hypothesis is true.
: Involves estimating the value of a population parameter.
A random variable is a variable whose possible values are numerical outcomes of a random phenomenon. There are two types:
Once probability is mastered, the lecture turns to the art of guessing.
Mathematical Statistics Lecture | A-Z REAL |
, which is a function of the data, to approximate the true value of
The final pillar of our lecture is hypothesis testing. This is the formal procedure for deciding between two competing claims: the null hypothesis and the alternative hypothesis. We use a test statistic to determine if the observed data is sufficiently extreme to warrant rejecting the null hypothesis. This process involves a delicate balance between Type I errors (false positives) and Type II errors (false negatives). The p-value, perhaps the most famous metric in statistics, tells us the probability of obtaining results at least as extreme as the ones observed, assuming the null hypothesis is true. mathematical statistics lecture
: Involves estimating the value of a population parameter. , which is a function of the data,
A random variable is a variable whose possible values are numerical outcomes of a random phenomenon. There are two types: This process involves a delicate balance between Type
Once probability is mastered, the lecture turns to the art of guessing.