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IOP2601 Assignment 3 Semester 1 2024 - DUE 2 May 2024
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IOP2601 Assignment 3 Semester 1 2024 - DUE 2 May 2024 QUESTIONS AND ANSWERS
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lOMoAR cPSD| 24668432[Date]
IOP2601 Assignment
3 Semester 1 2024 -
DUE 2 May 2024
QUESTIONS AND ANSWERS
, lOMoAR cPSD| 24668432
1
IOP2601/ASSIGNMENT 03/SEMESTER 1/2024
IOP2601 Assignment 3 Semester 1 2024 - DUE 2 May
2024
ASSESSMENT 03
Closing date: Tuesday, 2 May 2024 @ 23:00
Total: 50 marks
Study material: Lessons 11 to 14
Use computational formulas, where applicable, and round off the final answer to two decimal
places. (Values read from tables must be given unaltered.)
QUESTION 1 [12]
1.1 Indicate whether the following statements are True or False. Give reasons for your answer.
a) For this module, the level of significance is always given as 0,005. (2)
b) A Type I error is made by rejecting the null hypothesis when it is in fact true. (2)
c) If you get a negative value for the F-test, this means the test statistic falls inside the rejection
area. (2)
a) False
The level of significance (alpha), denoted by α, is not always set at 0.005. It can vary depending on the
specific hypothesis test being conducted and the desired balance between the two main types of errors
(Type I and Type II). Common choices for alpha include 0.05, 0.01, and 0.001, but researchers can
choose other values based on their field and research question.
b) True
A Type I error occurs when you reject the null hypothesis (H₀) even though it's actually true. In simpler
terms, you mistakenly conclude that there's a significant difference between groups or variables when
there really isn't. Setting a lower alpha level (like 0.005) reduces the chance of a Type I error but
increases the chance of a Type II error (failing to reject a false null hypothesis).
c) False
The F-test statistic is used to compare variances between groups. A negative value for the F-statistic
wouldn't make sense in this context. A positive value indicates that the variance between groups is
greater than the variance within groups, which might lead to rejecting the null hypothesis (depending on
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