Methods III

Question 1

In Hpyothesis testing, for which p-values is it likely to reject the null-hypothesis? (When p = P(observed effect | H₀)

Answer 1

For (very) low values of p, it is a high confindence of the null-hypothesis to be rejected.

Question 2

In Hpyothesis testing, for which p-values is it likely to not reject the null-hypothesis? (When p = P(observed effect | H₀)

Answer 2

For (very) high values of p, it is a low confindence and statistically insignificant.

Question 3

What are we testing in Hypothesis testing?

Answer 3

We are testing the likely hood of the measured difference between the means of a hypothesis and an actual sample.

Question 4

If we reject the null-hypothesis, does this mean we accept the alternative hypothesis?

Answer 4

No

Question 5

Do we reject the null-hypothesis if the p-value is above or under the significance level (alpha).

Answer 5

we reject when p-value is under (or equal to) alpha (p « a)

Question 6

Do we fail to reject the null-hypothesis if the p-value is above or under the significance level (alpha).

Answer 6

When p-value is above the significance level a, we fail to reject the null-hypothesis.

Question 7

In hypothesis testing, what is an Type II error?

Answer 7

When the truth is that H₀ false, but hypothesis-test incorrectly not rejects H₀

Question 8

In hypothesis testing, what is an Type I error?

Answer 8

When the truth is that H₀ is true, but hypothesis-test incorrectly rejects H₀

Question 9

In hypothesis testing, what would a false positive mean?

Answer 9

That the H₀ actually is true but it still was rejected. (the attempt to reject the null-hypo was (falsely) positive) (type I error)

Question 10

In hypothesis testing, what would a false negative mean?

Answer 10

That the H₀ actually is false but it still was failed to reject it. (the attempt to reject the null-hypo was (falsely) negative) (type II error)

Question 11

In hypothesis testing, what would a true negative mean?

Answer 11

That the H₀ was true and hence it was failed to reject it. (the attempt to reject the null-hypo was (correctly) negative)

Question 12

What is the chance of a Type II error in hypothesis testing?

Answer 12

This chance is equal to the area underneath the H₁ curve that lays to opposite site of the signififcane level. We call this ß.

Question 13

What is the chance of a Type I error in hypothesis testing?

Answer 13

This chance is equal to the significance level (alpha), because it represents the probability underneath the H₀ curve that is smaller than alpha.

Question 14

What is a t-test used for?

Answer 14

A t-test is used to define the chances of a curve in hypothesis testing.

Question 15

What formula is used for t-tests? (you should be able to indentify what parameters have what influence)

Answer 15

t = sqrt(n) * (mean(X) - mu) / (sigma). Where mean(X) is the x axis and mu is the mean of an hypothesis. Sigma is the standard deviation.

Question 16

Will an increase in sample size reduce or increase the Type II error of an t-test.

Answer 16

Reduce, as it sharpens the curve and hence reduces the area.

Question 17

Will an decrease in the alternative hypothesis (mu) reduce or increase the Type II error of an t-test?

Answer 17

Increase, When the curves are moved more closely, the area (thus chance) of a type II error increases.

Question 18

What change in variance will result in a higher type II error?

Answer 18

A higher variance, will allow for wider curves, and thus more area for a type II error.

Question 19

What can we change to improve t-test for hypothesis testing?

Answer 19

As variance and mean are defined by the data itself; we can only gather more data in order to improve our t-test.

Question 20

What is the power of a t-test?

Answer 20

1-P(type II) = 1 - ß

Question 21

What does statistical testing relate to?

Answer 21

It only relates to random error, systematic error is completely ignored.

Question 22

What is statistical validity?

Answer 22

Are our claims valid? Are the measured values really the practical truth?

Question 23

What is statistical reliability?

Answer 23

Are we confident about our claims? Is it likely to obtain the same results in an equal study?

Question 24

What is pseudoreplication?

Answer 24

Any two experimental units should be able to receive two different treatments, if they can’t they are probably sampling unit. Psuedoreplication: If we interpet this wrong in our observations, the false replication inflates the sample size, statistical power, etc.

Question 25

In retrospect to internal validity, what is selection bias?

Answer 25

There are already (unknown) differences between groups.

Question 26

In retrospect to internal validity, what is the threat of Repeated testing?

Answer 26

Subjects learn and remember between observations

Question 27

In retrospect to internal validity, what is the threat of Matuartion and learning?

Answer 27

Subjects change during the study, affecting the response.

Question 28

In retrospect to internal validity, what is the threat of Experimenter bias?

Answer 28

Researchers unknowingly affects subjects and their responses

Question 29

In validity, what is regarded an non-experiment?

Answer 29

We only observe existent data; we do not control the treatments or patients.

Question 30

In validity, what is regarded a quasi-experiment?

Answer 30

We can now control wich treatment is used, but that decision is not random: it might be dependent on the gender of a patient.

Question 31

In validity, what is regarded an true-experiment?

Answer 31

There is a assignment of random treatments to random patients. It’s all random, so it gives us the most usefull data.

Question 32

What is conclusion validity?

Answer 32

It reflects how valid the conclusion drawn in retrospect with the significance level of the outcome, etc.

Question 33

What are threats to conclusion validity?

Answer 33

Unreasonable assumptions or low statisitical power in analysis. Low reliablity of instruments or poor data documentation.

Question 34

What is internal validity?

Answer 34

How exhaustive and mutually exclusive is the outcome of the results? (are the possible thrid variables that influence the outcome?)

Question 35

What is contruct validity?

Answer 35

Are the variables that are being analysed correctly reflect the concepts of the study? (f.i. you cant use google searches to indicate strong economical behaviour.)

Question 36

What is external validity?

Answer 36

How well does our sample group represent a larger population? (generalisation)