Week 10 Kuracloud: Statistical Inference Flashcards

1
Q

Inferential statistics

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A

= the methods we use to infer the results from a study sample to the wider population.

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2
Q

Population/Target population

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A

= group from which sample is drawn

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3
Q

Sample

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A

= people who take part in study (participants)

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4
Q

For the sample to be representative of the population:

A

each person in the population of interest should have an equal chance of being selected in the sample

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5
Q

Sampling Frame

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A

= list of all people/objects in target population

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6
Q

Sampling distribution

=, 3

A

= frequency distribution of all means of samples of a population
- approximately normal distribution
- mean of sampling distribution is same as mean of population
- SD of sampling distribution = standard error of the mean

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7
Q

Standard error

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A

= variations in means from multiple sets of measurements, SD of sampling distribution
- can be estimated from single set of measurements:
SE = (standard deviation)/sqrt(n)
(standard deviation divided by square root of sample size)

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8
Q

Confidence Interval

=, 2

A

= range in which we are pretty sure the population parameter (e.g. means, medians, difference of means and differences of medians) lies
Depends on:
- variation within population (proprtional to confidence interval)
- size of sample (inversely proportional to confidence interval)

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9
Q

Methods for calculating confidence intervals

3

A
  • informal
  • traditional normal-based formulas: stated level of confidence affects confidence interval length
  • bootstrapping
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10
Q

95% confidence interval (95%CI)

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A

= 1.96 SE above and below sample mean
because normal distribution of sample means –> 95% of sample means are within 1.96 SE of distribution

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11
Q

95% confidence interval for sample proportions

=,

A

= sample proportion +/- 1.96 x SE
calculated from SE of a sample proportion

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12
Q

Comparing population means or proportions

2

e.g. means cancer in smoke vs nonsmoke in population

A
  • estimate differences in population means or proportions using sample means or proportions
  • calculate 95% confidence interval of measure of difference using standard error of differences in means or proportions
    95% confidence interval = mean difference +/- (1.96 x SE)
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13
Q

Methods of comparing 2 proportions

4,

A
  • absolute difference in 2 proportions
  • risk ratio
  • odds ratio
  • prevalence ratio
    if SE known, 95% confidence interval can be calculated for each
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14
Q

null hypothesis (H0)

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A

= no relationship between exposure and outcome

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15
Q

Alternative hypothesis (HA)

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A

= not null hypothesis

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16
Q

p-value

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A

= probability of getting observed result if null hypothesis is true (chance that observed estimate is result of sample variation)
p > 0.05: not significant: no/weak evidence against H0
p < 0.05: significant: evidence against H0 (justify rejection)
P < 0.01: highly significant: strong evidence against H0
P < 0.001: very highly significant: very strong evidence

17
Q

Statistical test for numerical outcome variables

4

A
  • 2 groups, paired observations: paired t-test (Wilcoxob signed rank test)
  • 2 independant groups: two-sample t-test, linear regression (Mann Whitney test)
  • > 2 groups: ANOVA, linear regression (Kruskall Wallis test)
  • numerical exposure variable: Pearson correlation, linear regression (Kendall’s rank correlation, Spearmans correlation)
18
Q

Statistical tests for binary outcome variables

3

A
  • 2 exposure groups: chi-squared test, logistic regression
  • > 2 exposure groups: chi-squared test, logistic regression
  • > 2 ordered exposure groups: chi-squared test, logistic regression
19
Q

If the 95% CI does not contain the null value:

A

p<0.05

20
Q

Value of no difference

A
  • comparing means: 0
  • comparing proportions using ratio: 1
21
Q

Sample estimate/effect size

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A

= difference between 2 groups
- difference of means
- difference of proportions
- ratio of proportions or odds