statistical definitions

Question 1

p- value

Answer 1

Used to quantify extent to which the sample estimate contradicts the null hypothesis.
between 0 and 1
the smaller the p-value the more evidence that the null hypothesis is false

traditionally 0.05 has been used as the threshold to reject or not reject the null hypothesis.

Question 2

null hypothesis

Answer 2

A null hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove

Question 3

interquartile range

Answer 3

• spans the values between the lower quartile (25th percentile) and the upper quartile (75th), that is the middle 50% of obs. Used to quantify variation or the amount of spread of the scores

Question 4

standard deviation

Answer 4

variation in score for the quantitative variables i.e. the spread of data around the mean.

‘average difference between the scores and the mean’

Question 5

standard error

Answer 5

quantifies the precision with which the true population parameter (the mean) is estimated.

• the smaller the standard error, the more precise the sample estimate of the true mean
• tells us how far on average the sample estimates of the mean would be from the true mean, if you carried out the study a large number of times using different samples of the same size from he same population

Question 6

95% confidence intervals

Answer 6

the range of values within which we can be 95% certain the true parameter of interest lies
e.g. we can be 95% sure that the true mean bone mineral content lies between these two values.

Question 7

Nominal

Answer 7

categories

Question 8

Quantitative

Answer 8

numbers increasing

Question 9

risk

Answer 9

the proportion of people in a single group who are suffering. Calculated by dividing the number of people who have the disease vs the total number of people

Question 10

relative risk

Answer 10

used to compare the risk between two groups and is calculate as the risk in one group divided by the risk in the other.

Question 11

odds

Answer 11

used to quantify how common binary characteristic for a single groups. calculates as the number of people with the characteristic of interest divided by the number of people without the characteristic.

Question 12

odds ratios

Answer 12

the ratio of odds in one group to the odds in another- calculated to compare groups

Question 13

no significant difference

Answer 13

the difference between values cannot be explained by dependent variables. There is little evidence that the true mean peak expiratory flow rate changed between the first and second measurement in the pop.

Question 14

risk difference

Answer 14

the absolute difference- e.g. the differences between scores in two groups. An absolute difference of 0 indicates no difference

Question 15

a postive difference

Answer 15

indicates greater risk in the first group

Question 16

a negative difference

Answer 16

indicates greater risk in the second group

Question 17

a relative risk of 1

Answer 17

indicates no difference between the groups

Question 18

a relative risk of above 1

Answer 18

indicates greater risk in the first group

Question 19

a relative risk below 1

Answer 19

greater risk in second group

Question 20

if odds are 1

Answer 20

the people are just as likely to have a the characteristic as they are not to