# 08. Statistics Flashcards

What is the outcome result of a parametric test?

P-value

What type of test would be appropriate to compare two groups of patients for incidence of hypotension?

The incidence of hypotension, ie it did/did not occur, would be categorical (or qualitative) data thus requiring a non-parametric test,

eg Chi squared test, McNemars

Which is more powerful, parametric or non-parametric?

Parametric

They are more powerful, ie they are more likely to show a difference that really exists.

How are confidence intervals derived?

Are they proportional to sample size?

CI’s are derived from the Standard Error.

Standard Error is inversely proportional to the sample size.

- What do confidence intervals indicate?
- What is the meaning if confidence intervals overlap?
- How do rare complications affect the confidence interval?

Confidence Intervals indicate the range within which the true value will plausibly lie.

If the confidence intervals of 2 groups overlap, the true value may be identical (whereas the mean of each group may be very different).

For rare complications, the CI’s will again give a range in which the true incidence will plausibly lie.

Can the ASA status of a group of patients be best described by median and standard deviation?

ASA is ordinal data (a subtype of qualitative or categorical data).

Median and Standard Deviation are reserved for quantiative data that has a normal distribution.

Can ASA status be compared by chi-squared test?

Yes because it is non-parametric

Does SD increase as sample size increase?

No

Standard deviation is calculated as?

The square root of the variance

If p = 0.05 for a comparison of treatments: T/F

A. there is a 95% chance that there is no difference between the treatments.

False.

A p-value of 0.05 would be considered significant; thus there is evidence that the treatments are not equal.

If p = 0.05 for a comparison of treatments: T/F

The null hypothesis is incorrect.

False.

As the p value decreases the null hypothesis will be increasingly questioned, but at no point can we say it is definitely false.

If p = 0.05 for a comparison of treatments: T/F

The chance of a false negative is 5%

The chance of a false negative is 1 - Power.

If p = 0.05 for a comparison of treatments: T/F

We can conclude that one treatment is more effective

False.

Although this result is statistically significant, we have no information as to whether it is clinically significant.

If p=0.05 for a comparison of two treatments: T/F

The data we have observed would only occur 5% of the time or less if the treatments were equally effective

TRUE

How can you show the spread in non-normal data?

Using interquartile range.

Non-normal data can be divided into 4 Quartiles which can be used to show the range of spread (whereas SD is used for normal data).

The range of values that include the 2nd and 3rd quartiles represents the half of the data lying closest to the central value - this is the Interquartile Range.