# Statistical hypothesis testing Flashcards

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

- can establish effectiveness if new Rx
- make Dx
- risk factors for disease
- characteristics for disease

What is null hypothesis significance testing (NHST?

= statistical hypothesis testing

comparison of null and alternative hypothesis generated P-value

What is the function of a P-value?

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

What are the key features of an RCT study design?

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

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

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 ±

What do statistical hypothesis testing measure?

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

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

Does the ‘sample estimate’ support the assumption?

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

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

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)

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

LEARN **

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

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

Should be be defined prior to collecting sample data

How do statistical and research hypotheses differ?

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

What is a null hypothesis?

= H(0)

in population where sample taken, there is no difference between the intervention and control groups

in terms of mean data values

What does statistical hypothesis testing usually include?

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

What is the alternative hypothesis?

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

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

there are 2 potential outcomes:

Int > Ctrl

OR

Ctrl > Int

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

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

What is the P-value?

probability of OBTAINING sample difference in mean data point under null hypothesis

i.e. difference in mean data points between groups = 0.00

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

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)