# W4 - Confidence Intervals & Hypothesis Testing Flashcards

1
Q

Do we want a higher or lower SE?

A

Lower SE.

2
Q

How is SE calculated?

A

SD / (square root of sample size)

3
Q

What are confidence intervals (CI)?

A

Range of values between which you would expect the pop mean to sit.

95% likely to include the REAL pop mean.

4
Q

What does the width of the confident interval (CI) indicate?

A

Uncertainty about the unknown pop mean.

5
Q

What might a very wide CI indicate?

A

More data should be collected before anything can be determined.

6
Q

What type of width (bigger or smaller) would you want the CI for increased certainty?

A

Smaller width

7
Q

How is the lower boundary of 95% CI calculated?

A

Mean of sample - (1.96 x SE)

8
Q

How is the upper boundary of 95% CI calculated?

A

Mean of sample + (1.96 x SE)

9
Q

What are the 2 types of research Q?

A

Are the 2 variables related?

Are the 2 means different?

10
Q

TYPES OF RESEARCH Q

Are the 2 variables related?

What are the 2 ways of showing this?

A

Correlation (r)

Regression

11
Q

TYPES OF RESEARCH Q

What test would you use for the following type of research Q:

Are the 2 means different?

A

T-test

12
Q

What is the null hypothesis?

A

States there will be NO difference between means or NO relationship between variables

H(little 0)

13
Q

What does the alternative hypothesis (Ha or H1) state?

A

That there WILL be a difference between means or there WILL be a relationship between variables.

H(little 1)

14
Q

Can you prove that the alternative hypothesis is true?

A

no

Instead, can demonstrate that its more likely than the null hypothesis.

15
Q

What determines whether the null hypothesis is rejected in favour of the alternative hypothesis or if we fail to reject the null hypothesis?

A

Significance level + the probability values

16
Q

What are the types of alternative hypothesis?

A

Directional

Non-directional

17
Q

What is the alpha level?

A

(a.k.a significance level)

Probability of rejecting null hypothesis when it’s actually true.

i.e a significance level of 0.05 indicates a 5% risk of concluding there’s a difference when there’s no actually difference.

18
Q

How is a type 1 error (a.k.a false +ive) created in regards to the null hypothesis?

A

You conclude there’s a difference, when in reality there isn’t.

19
Q

What is the probability of making a type 1 error represented by?

A

Alpha level

20
Q

How is a type 2 error created in regards to the null hypothesis?

A

Researcher concludes there’s NO difference when there actually is.

Could happen if their sample size is too small.

21
Q

What does the p-value provide a measure of?

A

Strength of evidence vs null hypothesis.

22
Q

What does a smaller p-value mean?

A

Smaller probability that the observed difference was due to chance (unrepresentative samples)

23
Q

What happens when p=0.03?

A

REJECT null hypothesis + interpret difference as ‘real’.

Yet there is a 3% chance that the diff has occurred by chance (due to sampling variation).

24
Q

What does a smaller P value result in?

A

Stronger evidence vs the null hypothesis