Statistical hypothesis testing Flashcards

1
Q

What is the purpose of using comparison of groups in research?

A
• can establish effectiveness if new Rx
• make Dx
• risk factors for disease
• characteristics for disease
2
Q

What is null hypothesis significance testing (NHST?

A

= statistical hypothesis testing

comparison of null and alternative hypothesis generated P-value

3
Q

What is the function of a P-value?

A

used to ascertain whether sample data provides evidence for difference between the groups in the population

4
Q

What are the key features of an RCT study design?

A

Samples taken from a given population (ideal: that this sample is representative)

Parallel study design

Sample groups are RANDOMLY allocated to either intervention/exposure group or to placebo/non-intervention group

Compare treatment efficacy at the end time point between 2 groups

May also do another leg of RCT study where the groups/interventions are swapped

5
Q

How is the ‘sample mean difference’ in a RCT calculated?

A

Sample mean difference is an ESTIMATE for POPULATION PARAMETER

calculated either by:
a) intervention - control
or
b) control - intervention

numerical value will be the same for both, just if it is ±

6
Q

What do statistical hypothesis testing measure?

A

the extent to which the study sample estimate (i.e. the data) reflects the difference for the relevant (wider) population
measured by the P-value

7
Q

What questions are we trying to answer by using statistical hypothesis testing?

A

Does the ‘sample estimate’ support the assumption?

Does ‘sample estimate’ reflect a difference? (Data is used as evidence here)

8
Q

What is meant by ‘equipose’ as the starting point for statistical hypothesis testing?

A

equipose = null hypothesis
there is no difference between the 2 sample groups
this is the traditional starting point for statistical hypothesis testing

question we’re trying to answer through stats is does the data/sample estimate go in favour of null hypothesis (H0) or is in in favour of an alternative hypothesis (where there is a difference)

9
Q

What are the main formal steps in performing statistical hypothesis testing?

LEARN **

A
1. Define statistical null hypothesis and alternative hypothesis
2. obtain (representative) sample from the population
3. Calculate value of the test statistic (using sample) specific to null hypothesis (using data)
4. Use test statistic to derive probability (P-value) that quantified whether null hypothesis should be supported or rejected
5. Interpret probability (P-value) and sample data
10
Q

When are the null and alternative hypotheses defined relative to the study?

A

Should be be defined prior to collecting sample data

11
Q

How do statistical and research hypotheses differ?

A

Statistical hypotheses are very formal

Research hypotheses may result from a pre-conceived idea of a direction or association
(usually informed by prior data). These are usually postulated and inform in experimental design

12
Q

What is a null hypothesis?

A

= H(0)
in population where sample taken, there is no difference between the intervention and control groups
in terms of mean data values

13
Q

What does statistical hypothesis testing usually include?

A
• extent to which sample estimates supports null hypothesis
• measured by probability (= P-value)
• evidence difference in mean effectiveness (intervention vs. cntrl) in population
14
Q

What is the alternative hypothesis?

A
```= H(A)
In population where sample taken, there is a difference between intervention and control groups
such that either
Int > Control OR
Int < Ctrl```
15
Q

Why is the alternative hypothesis considered to be a 2-sided concept?

A

there are 2 potential outcomes:
Int > Ctrl
OR
Ctrl > Int

16
Q

How does P-value inform the decision on whether sample estimate lends support to Null or alternative hypotheses?

A

P-value is a probability
between 0 < P-value < 1.0

Strength of supporting null hypothesis:

• LIKELY: P-value = 1.0
• UNLIKELY: P-value = 0.0
17
Q

What is the P-value?

A

probability of OBTAINING sample difference in mean data point under null hypothesis
i.e. difference in mean data points between groups = 0.00

18
Q

What is the relationship between the P-value and supporting the null hypothesis?

A

large P-value (near 1.0) MORE LIKELY to support null hypothesis

small P-value ((near 0.0) LESS LIKELY to support null hypothesis
(^ more likely to support alternative hypothesis)

19
Q

Why is the P-value conceptually difficult in most study designs?

A

we usually only have one event, but P-value is in its truest form based on repeated sampling

20
Q

What is the (widely used)

‘critical level of significance?’

A

P=0.05 cut-off

i.e. 5% significance

21
Q

What is the practical caveat of using P=0.05 as the critical level of significance?

A

It is treated as a dichotomy
e.g. P<0.05 null hypothesis is automatically rejected and the difference in sample data is taken to be as important clinically as well as statistically
this is not always the case

22
Q

What is the main issue with using statistical hypothesis testing?

A

People often take statistical significance as a proxy for clinical significance
(rarely one can substitute another. P-value should not be used to assess for clinical significance)

23
Q

What inference can be made using the P-value?

A

Statistical significance ONLY

not clinical significance

24
Q

What is the P-value based on?

A
• theoretical concept
• using repeated sample from the population
• using the same sample size and conditions for each repeat
• each sample should have a unique mean difference
• there should be variation between sample estimates
25
Q

What is hypothesis testing based on?

A

Theoretical concept of REPEATED SAMPLING

26
Q

How is P-value calculated?

A
• using statistical test/formula
• the specific test/formula is chosen based on data type and number of variables/groups
• MAGNITUDE determines P-value
27
Q

What happens to the t-statistic and P-value when the mean difference between groups increases?

A

t-statistic increases

P-value decreases

(MORE SIGNIFICANT DIFFERENCE)

28
Q

How is the test statistic determined?

A

= t statistic

= (mean difference between groups) / SE of that difference

29
Q

When can a (Student’s or paired) t-test be used?

A

when measuring a continuous variable between groups

30
Q

What is the distribution of t test statistic?

A

similar to ‘normal distribution’

BUT with GREATER PEAK height

31
Q

At what value of n does the distribution become a ‘normal distribution?’

A

As n approaches infinity

or from >120

32
Q

According to the P<0.05 cut-off, what proportion of samples are rejected in the null hypothesis?

A

if P<0.05 for critical significance
then we are rejecting 5% of samples
i.e. this is 2.5% at each tail extreme of the distribution curve (2x2.5%)

33
Q

What is the explanation behind rejecting null if sample estimate is in (5%) tails of distribution?

A

sample estimates found in these extreme value ranges are of a large effect size.
In order for this to exist, it cannot be due to random sampling as in the null hypothesis and must be down to an actual effect
Therefore, in order for the effect to exist, the null must be rejected

34
Q

Can a hypothesis ever be true or false?

A

no, we can never say for sure. Hypothesis is a theoretical concept - cannot be definitively measured

Statistical significance DOES NOT EQUAL clinical significance

We can only comment on if there is evidence of a difference between our sample estimates and whether this statistically is in the favour of the null or alternative H

35
Q

What kind of errors can be made in statistical hypothesis testing?

A

Type 1 error

Type 2 error

Errors are conceptual, so can never be certain that either error has been made

36
Q

What is a type 1 error?

A

Null hypothesis is rejected incorrectly

(i.e. saying that there is a difference but there is not)

Decision is based on sample data

37
Q

What is a type 2 error?

A

Null hypothesis is accepted/favoured incorrectly

(i.e. saying that there is no difference but there is)

Decision is based on sample data

38
Q

What can be done to reduce the probability of making a type I or type II error?

A
• increase sample size

- improve sampling method

39
Q

What are the maximum chances of making a type 1 error in a single NHST study?

A

NHST = null hypothesis significance testing

5% (if critical significance is set to P=0.05)

40
Q

Why may a type I or type II error be made in a NHST study?

A

sample may not be representative or appropriate
this creates undue variation not relating to the causal effect
therefore dilutes the statistical effect

41
Q

What is the risk when making an inference about the population using a sample estimate?

A

making a sampling error

this causes INTRINSIC VARIABILITY

42
Q

Which inferences based on statistical; hypothesis testing are made incorrectly?

A
• statistical inference being taken as biological or clinical significance
• not significant means not important
43
Q

Which type of probability errors are prevalent in research?

A

type 1 errors (highly prevalent)

due to inappropriate interpretation of stats etc

44
Q

What are the 3 concepts in the triad of significance?

A
• statistical (often based on sample AVERAGE, not individual)
• patient (need to apply to the INDIVIDUAL patient)
• clinical
(types of significance)
45
Q

What does P-value inherently quantify?

A

Is a measure of probability

= Strength of evidence to support Null hypothesis
when significance is set at 5%

46
Q

How should NHSTs be inferred?

A

only in context of interpreting the statistics

SHOULD NOT be used in interpreting the biological or clinical significance