Stats Concepts

Question 1

What is a variable?

Answer 1

A feature of individual units within a study (e.g. people); something that we can observe or measure.

Question 2

What are the four things an outcome could be?

Answer 2

An observation (at one moment in time - attained weight of a baby at 6 months; mortality, status (dead/alive)), a time to an event (that may or may not happen-Time to death), a count (independent of time - number of measles cases), a rate (dependent on time - No of deaths per 1000 person-years)

Question 3

What is binary data?

Answer 3

Categorical (not numerical) data which only has 2 alternatives/options - an example would be dead/alive

Question 4

What is nominal data?

Answer 4

Categorical (not numerical) data which has more than 2 alternatives/options but which has no natural order - classic examples are ethnic groups or blood type

Question 5

What is ordinal data?

Answer 5

Categorical (not numerical) data which has more than 2 alternatives/options and a natural order to it; for example - hypertensive/borderline/normal/hypotensive

Question 6

What is discrete data?

Answer 6

Numerical data (quantitative) that is a count - for example, number of measles cases

Question 7

What is continuous data?

Answer 7

Numerical data (quantitative) where there is an infinite number of values the data can take - for example, blood pressure, weight, age

Question 8

What is positively skewed data?

Answer 8

Data who’s frequency distribution is skewed to the right on the x axis

Question 9

What is negatively skewed data?

Answer 9

Data who’s frequency distribution is skewed to the left on the x axis

Question 10

What are some examples of continuous probability distributions?

Answer 10

Normal distribution, t-distribution, f-distribution, chi-squared distribution

Question 11

What are some examples of discrete probability distributions?

Answer 11

Binomal, poisson, uniform

Question 12

What is meant by the range of data?

Answer 12

Simply the highest score minus the lower score; it is the range of scores you would see in your sample

Question 13

What is VARIANCE?

Answer 13

The average squared distribution from the mean

Question 14

What does variance tell us?

Answer 14

On average, how much the scores are distributed around the mean

Question 15

What is STANDARD DEVIATION?

Answer 15

It is the square root of the variance

Question 16

What happens to the mean in the context of a skewed distribution?

Answer 16

It no longer gives a good impression on the central tendency of the observations

Question 17

What is a more appropriate measure than the mean for skewed distributions?

Answer 17

When data are skewed, it is more appropriate to use the median and interquartile range (75th to 25th quartile) as your descriptive statistics. If data is positively skewed, a log transformation will also help

Question 18

What is the only time the mean is appropriate?

Answer 18

When the data is normally distributed

Question 19

What are population values?

Answer 19

The true values of a measure in a population. They define the population.
• Mean, μ
• standard deviation, σ

Question 20

What is a sample statistic?

Answer 20

The value of the measure in a sample of the population. It is calculated from the observations in the sample
• Sample mean, x̄
• Sample standard deviation (SD), s

Question 21

What is sampling error?

Answer 21

Information gained from a single sample is the “best estimate” of what is true in the population
• In truth, the sample statistic may be somewhat larger or smaller than the true population value (i.e. uncertainty)
• This is due to sampling error

Question 22

What is standard error?

Answer 22

It is the measure of the accuracy of the sample estimate. It calculates how far from the true (but unknown) population value the sample estimate is likely to be - basically, how large an error we are likely to be making.
The standard error of the mean would be calculated as: = SD/ square root of n = s/ square root of n

Question 23

What are the two main methods of statistical inference?

Answer 23

Hypothesis testing (significance testing)
Estimation (confidence intervals)

Question 24

What does low SD indicate?

Answer 24

Data points are close to the mean

Question 25

What does high SD indicate?

Answer 25

Data points are far from the mean

Question 26

95 of values lie within how many SDs of the mean?

Answer 26

2 (1.96 to be precise)

Question 27

How do you calculate a CI range of values?

Answer 27

95% of effect estimates for ‘large’ samples have values
between(effect - 1.96 SE) and (effect + 1.96 SE). SO, you would calculate as:
For 95% CI for diff = MEAN ± (1.96 x SE) = upper value
= MEAN - (1.96 x SE) = lower value
95% CI (upper value to lower value)

Question 28

Describe the relationship between p values and confidence intervals

Answer 28

If the ‘no effect’ value falls outside the CI then the result is statistically significant
• Confidence intervals and P-values present complementary information
• Confidence intervals show the range within which the true treatment effect is likely to lie
• P-values measure the strength of the evidence against a hypothesis of particular interest: the null hypothesis

Question 29

How is a CI determined?

Answer 29

CI is determined by the Standard Error – a measure that combines SD and sample size, n. Standard Error (SE) = SD/ square root of n

Question 30

What is the definition of a CI?

Answer 30

A confidence interval provides a range of plausible values for the POPULATION mean, not the SAMPLE mean

Question 31

What does a p-value measure?

Answer 31

The strength of the evidence against a

hypothesis of particular interest: the null hypothesis

Question 32

What is the definition of incidence?

Answer 32

The number of NEW cases of a disease/condition during a population at risk of developing the disease/condition during a specified time period

Question 33

How is cumulative incidence different from incidence rate?

Answer 33

Incidence rate uses person-time (the sum of the disease-free time) as the denominator, incidence uses the number of people at risk of developing disease/condition during a specified time period

Question 34

what is the definition of cumulative incidence?

Answer 34

o The cumulative incidence or risk of a disease is the
probability that the disease occurs during a specified time period.
o Equivalently, cumulative incidence can be defined as the percentage of the at risk population in which the disease occurs during a specified time period.

Question 35

What is the question that cumulative incidence answers?

Answer 35

“what is the probability or chance that an individual

develops the outcome in a defined period of time?”

Question 36

When do we use the incidence rate (sometimes called incidence density) instead of simply the incidence?

Answer 36

When all people are not observed for the full time
period or not at risk for the full time period we need to
consider “person-time” at risk and report the incidence
rate (also sometimes called incidence density

Question 37

What question does incidence rate answer?

Answer 37

“at what rate are new cases of the disease occurring within the at risk population”

Question 38

What is the definition of prevalence, and how is it calculated?

Answer 38

Prevalence= Number of cases observed at time t /

Total number of individuals at time t

Question 39

What question does prevalence answer?

Answer 39

“what fraction of the group is affected at this moment in time?”

Question 40

What is the definition of a p value

Answer 40

A p value is the probability of having observed our data (or more extreme data) given that the null hypothesis is true.