ISDS702 - Week 2 - Hypothesis Testing Notes
Overview
Good Morning,
This week we will talk about the concept of hypothesis testing and conduct z and t tests to practice this
concept. There are some theory and definitions this week. Please make sure you understand these theories
and definitions before you take the quiz. If you have any questions ask me or your class mates. I am planning to
hold another meeting this Thursday at 5 pm (central time) for your questions. I will send you a meeting
invitation in an email.
Apart from the quiz, please do not forget to complete the Excel assignment.
Best of luck!
Dr. Koksal
Week 1 Quiz Overview
Dear Students,
Due to school’s no return policy, quizzes in this course are not open to review after they are graded. However, I
will provide general feedback for each quiz so that you can refer back to the material accordingly.
Please find below an overview of Week 1 quiz.
The average grade for Week 1 quiz was 52.67 out of 60. This is almost 88%, congratulations!
I went through the quiz to identify the questions with which students struggled the most. According to the
results, one of the concepts students struggled with is discrete/continuous random variables. Please refer to
the course video to review these concepts.
Just as a summary, continuous random variables can take any value within an interval. Discrete random
variables on the other hand can only take certain values.
For example, say a scientist is studying wolves. Weight is an example of a continuous random variable, because
the weight of a wolf can take any value between, let’s say 50 and 180 pounds. So a wolf can weigh 120 pounds,
or 120.2 pounds, or 120.22 pounds, etc.
On the other hand, size of a wolf pack is a discrete variable. For example we can talk about a pack of 9 wolves,
but it makes little sense to talk about a pack of 9.2 wolves. This is because 9.2 is not a value the size variable
can take.
I also noticed that the z score is a concept some students struggled with. The z score is a measure of the
distance in standard deviations between any given value and the mean of the sample/population from which
the value is drawn. For example, if the z score of a variable X is 2, we say that X is 2 standard deviations above
the mean. If the z score is -2, we say that X is 2 standard deviations below the mean.
z = (x - µ)/σ
Looking at the formula above, we can say that one is more likely to find a larger z score when the standard
deviation is small (when the denominator is small). Similarly, a large numerator (a greater difference between
a given X value and its mean) will also lead to a larger z score.
Keep in mind that the probability of an event and the probability of the complement of that event add up to 1.
For instance, let's say we have an event X. Assume that event Y is the complement of event X. Than P(X) + P(Y)
= 1.

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- Fall '15
- Null hypothesis, Statistical hypothesis testing