10. Interpreting Evidence 1

Question 1

Why do we need Statistics to interpret evidence?

Answer 1

Variability:

1. Between people..
   e. g differential effectiveness of treatment
   e. g. Do or do not develop particular side-effect e.g. differential response to environment
2. Within people…
   e. g. measures of blood pressure over a day e.g. strength of left and right hands

Question 2

Normal versus skewed distributions

Answer 2

Normal distribution= Mean/median and mode at the same value in the middle (i.e. is perfectly symmetrical)

Negatively skewed= Mean

Question 3

Odds ratios:

Use?

Answer 3

Used in case-control studies or observational studies and regression models

Question 4

What defines the histogram normal distribution?

Answer 4

Around 68% of observations within 1SD of mean

Approx 95% of observations within 2SD of the mean

Question 5

When describing location and variability…
Mean –uses _____ data but can be influenced by outliers
Median –____ influenced by outliers, but doesn’t use all data (less informative)

Answer 5

Mean –uses all data but can be influenced by outliers
Median –not influenced by outliers, but doesn’t use all data (less
informative)

Question 6

When describing location and variability…
Mean –uses _____ data but can be influenced by outliers
Median –____ influenced by outliers, but doesn’t use all data (less informative)

Answer 6

Mean –uses all data but can be influenced by outliers
Median –not influenced by outliers, but doesn’t use all data (less
informative)

Question 7

Risk =

Answer 7

Risk =Number with disease / total number at risk

Question 8

ARR=

Answer 8

ARR= Risk 1 - Risk 2

Question 9

RRR =

Answer 9

RRR= Risk 1/Risk2

Independent of the original prevalence.

Question 10

NNT=

Answer 10

NNT= 1/ARR

Question 11

Odds ratio (OR) =

Answer 11

Odds if case/ odds if control

Question 12

How to calculate odds?

Answer 12

One type of group/ Other type

Question 13

• If odds are equal in case and control group OR=

Answer 13

1

Question 14

What is another name for baseline risk

Answer 14

Prevalence

Question 15

When is it better to use RR instead of OR?

Answer 15

When events are common

Question 16

What is a random sample?

Answer 16

In practice we can’t take measurements on every individual. We take a sample –preferably a random sample, that is representative of the population in which we are interested

Question 17

Larger samples mean more…

Answer 17

confidence

Question 18

What is standard error of the mean? (SE)

Meaning of large and small SE?

Answer 18

If we took repeated samples, the variability of the sample means could be measured
• A large SE indicates that there is much variability in sample
means; that many lie a long way from the population mean
• A small SE indicates there is not much variability between the sample means

Larger sample size = smaller SE

Question 19

SE vs SD?

Answer 19

SE is always smaller than SD because there is less variability between sample means than between individual values.

Question 20

What is a confidence interval?

Answer 20

A confidence interval gives a range of values, estimated from the sample data, which is likely to include “true” parameter

Question 21

95% Confidence interval= sample parameter +/- 1.96*SE

Answer 21

95% Confidence interval= sample mean +/- 1.96*SE

Question 22

Importance of CI?

Answer 22

Powerful tool for making decisions about whether observed differences are likely to be due to chance alone, or likely to be a true effect.

Question 23

Null hypothesis?

Answer 23

(in a statistical test) the hypothesis that there is no significant difference between specified populations, any observed difference being due to sampling or experimental error.

Question 24

Alternative hypothesis?

Answer 24

The alternative hypothesis is the hypothesis used in hypothesis testing that is contrary to the null hypothesis. It is usually taken to be that the observations are the result of a real effect (with some amount of chance variation superposed).