Lecture 3: Basics of Inferential Statistics i

Question 1

What are the Methods of inferential statistics?

Answer 1

Estimation

Hypothesis testing

Question 2

What is Estimation in inferential statistics?

Answer 2

Point and interval estimation

Question 3

What is Hypothesis testing inferential statistics?

Answer 3

Hypothesis testing in statistics is a way to test the results of a survey or experiment to see if you have meaningful results.

You’re basically testing whether your results are valid by figuring out the probability that your results have happened by chance.
•The smaller the probability: the more convinced we are that our results reflect the truth and is statistically significant

Question 4

In Hypothesis testing, the smaller the probability: the more convinced we are that our results reflect the truth and is statistically significant
True or false?

Answer 4

True

Question 5

What are the 5 steps of Hypothesis testing?

Answer 5

1- State the null hypothesis (H0)
2- State the alternative hypothesis (H1 or HA )
3- Choose your significance level, what kind of test you need to perform
4- Find the p-value
5- Reach a conclusion

Question 6

What is the null Hypothesis?

What does it state?

Answer 6

H0

It states: There is no difference things are the same as each other or the same as a theoretical expectation

Question 7

What is the alternative hypothesis?

What does it state?

Answer 7

Ha or H1

It states that things are different from each other, or different from a theoretical expectation.

Question 8

What is the significance level?

Answer 8

α (alpha)

Is the probability that results could have occurred by chance.

Question 9

When do we say the event is significant?

Answer 9

If the level is quite low, that is, the probability of occurring by chance is quite small, we say the event is significant.

This is a threshold of the amount of error we are willing to accept.

Usually below 0.05 (5%)

Question 10

What are P values?

Answer 10

• P values are used to determine statistical significance in a hypothesis test.
• Probability that an effect/difference is due to chance
• Probability of Type I error or α (alpha) error

Question 11

What does it mean If P < significance value?

Answer 11

It means obtaining that result purely by chance is small

So there is a significant difference

Question 12

What does it mean If P value > 0.05?

Answer 12

The probability that results are purely do to chance is greater than 5%, so conclude no significance

So no significant difference

Question 13

What is Type I error?

Answer 13

False positive results - α error

Falsely concluding a statistically significant relationship exists when in fact it does not.

Acceptable error at 0.05

Question 14

What causes type I error?

Answer 14

• Type I alpha error is due to ‘random’ chance associated with sampling.
• “Fishing Expedition”: If you perform multiple tests, the chance of random error increases.

Question 15

What is Fishing Expedition ?

Answer 15

If you perform multiple tests, the chance of random error increases.

Question 16

What is Type II error?

Answer 16

FALSE NEGATIVE RESULTS - β error

Set at 0.2

Question 17

What is the Power of a test?

Answer 17

POWER= 1 - β

= 0.8 or 80%

Question 18

What cause Type II error?

Answer 18

•Sample size (total and within group)
–Small groups have lower power
–Unbalanced groups have lower power

•The effect size
–It’s easier to find a LARGE effect.

Question 19

What is the Power at a test ?

Answer 19

The ability to reject the null hypothesis (H0) when its false

The ability to correctly to find a true effect

Power = 1-𝜷 (it is the inverse of the likelihood of making a Type II error)

Question 20

If there is a statistically significant difference, is it clinically relevant

True or false?

Answer 20

True

This is particularly important if the sample size is very large

Question 21

Statistical significance implies clinical importance

True or false?

Answer 21

False

Statistical significance does not imply clinical importance

Question 22

Statistical significance ≠ clinical significance

True or false?

Answer 22

True

Question 23

If there is no statistically significant difference it doesn’t necessarily mean there isn’t a difference

True or false?

Answer 23

True, consider the sample size in conjunction with the p-value.

It might just mean there wasn’t enough evidence

Question 24

What is the concento of Statistical significance ≠ clinical significance?

Answer 24

• Is there a difference vs. does this difference matter?

* A statistically significant effect does not always imply a clinical and public health significance

Question 25

What are the 3 things you need to do when reading am academic paper?

Answer 25

• Always consider what the research question is
• Estimate: The measure of effect (mean, mean difference, risk ratio, odds ratio)

•Finally, what is the P value?
–Significant - yes/no
–Due to chance - yes/no
–Significance level: is it 0.05 or otherwise?

Question 26

P value indicates statistical significance but not clinical significance

True or false?

Answer 26

True

Question 27

Type I or type II error occurs when we are wrong in our conclusions

True or false

Answer 27

True