Theme 3 - stats, how we use it to interpret our data

Question 1

give an example of how biology has an inherent variability in its data

Answer 1

e.g. women aged 50-70 are screened every three years for breast cancer, 4 in 100 will be positive but under further examination, only 1 in 4 will be a confirmed case

Question 2

what does standard error mean do?

Answer 2

allows us to determine how representative an average is to the whole population

Question 3

what is the definition of standard deviation?

Answer 3

the difference between the data values and the mean so the further a data point is from the average, the larger the SD

Question 4

how does sample size affect the sample mean?

Answer 4

the larger the sample, the closer the sample mean is to the true mean

Question 5

what is the central limit theorem?

Answer 5

the idea that the larger the sample, the closer it gets to the true mean and so shows us it is normally distributed

Question 6

how can you turn data into a normal distribution?

Answer 6

by repeatedly sampling the data set, finding the mean and plotting it to make it sample means

Question 7

how do you calculate standard error mean (SEM)?

Answer 7

by taking the SD and dividing it by the sample size

Question 8

what is the SEM?

Answer 8

a reflection of how accurate the sample is to the population

Question 9

what do length of inferential error bars suggest?

Answer 9

how much uncertainty there is in the data:
-wide bars indicate large error
-short inferential bards indicate high precision

Question 10

what is n in stats?

Answer 10

the number of independent subjects in an experiment and not the number of replicates

Question 11

what are the rules for error bars?

Answer 11

-they’re meaningless unless defined in the figure legend
-the n number should always be stated
-they should only be shown for independently repeated experiments and never for replicates

Question 12

what are the two stats tests that will test for normal distribution and how do you know what to use whem

Answer 12

-shapiro wilk test (for smaller sample sizes)
-kolmogorv-smirnov test (for sample sizes above 50)

Question 13

what is the null hypothesis?

Answer 13

there is no difference between the populations/ there’s no effect to be observed

Question 14

what does the experimental hypothesis state?

Answer 14

there is a difference between our populations/ an effect is observed

Question 15

what determines the validity of a hypothesis?

Answer 15

the inability to prove that it is false

Question 16

what is a control group? when might it need to be modified?

Answer 16

-a control group is a group where as many variables as possible are kept the same so that the only thing that is different is the experiment variable (the thing being tested)
-needs to be modified in observational studies by including adjustments in the statistical analysis to account for the confounding variables

Question 17

how does randomisation in a blind study work?

Answer 17

1. details of everyone taking part are put into a computer
2. the computer puts each person into a treatment group at random
3. the computer programme takes into account details such as age and e.g. stage of cancer to make sure all groups are as similar as possible

Question 18

what is a statistical test?

Answer 18

a test to determine if the observed finding are applicable to the wider population and not simply due to chance

Question 19

what is a type 1 error in statistical infernece?

Answer 19

a false positive error where there us no difference but we see one so we should have accepted the null hypothesis instead of reject

Question 20

what is a type 2 error?

Answer 20

a false negative error where there was a difference but did not detect it so we should have rejected the null hypothesis instead of accept it

Question 21

what is the alpha value?

Answer 21

the chance of making a type 1 error (often referred to as the p value)

Question 22

how is the alpha value different to the p value in statistical inference?

Answer 22

the alpha value is prior to the study whereas the p value is the observed result after the study is completed

Question 23

what is the beta value? how can you use this to find the ‘power’ of the study?

Answer 23

the chance of making a type 2 error and the power of the study is the inverse of the beta value

Question 24

why will the p value never be 0?

Answer 24

there is always a chance of making a type 1 error

Question 25

what is the definition of the p value?

Answer 25

the probability of the data/ observations arising due to chance when the null hypothesis is true

Question 26

what does it mean when the p value is less that 0.05?

Answer 26

the p value is sufficiently low and the so we can be confident the result being seen is real and not due to chance hence its statisticaly significant

Question 27

what is definition of sensitivity?

Answer 27

the proportion of diseased individuals that are correctly identified to have the condition

Question 28

what is sensitivity as a porportion?

Answer 28

a/(a+c)

Question 29

specificity definition

Answer 29

the proportion of non-diseased individuals that are correctly identified to not have the condition

Question 30

what is specificity as a proportion?

Answer 30

d/(b+d)

Question 31

what is the positive predictive value (PPV)?

Answer 31

the proportion of individuals with a positive test result that actually have the disease

Question 32

what is the equation for proportion of positive predictive value PPV?

Answer 32

a/(a+b)

Question 33

what is the negative predictive value (NPV)

Answer 33

the proportion of individuals with a negative test result that actually do not have the disease

Question 34

what is the equation for negative predictive value (NPV) as a proportion?

Answer 34

d/(c+d)

Question 35

what should the sensitivities and specificities of a good test include?

Answer 35

high sensitivities and specificities

Question 36

how does sensitivity and specificity affect ppv and npv

Answer 36

-high sensitivity = high ppv
-high specificity = high npv

Question 37

how are ppv and npv influenced by prevalence of the condition?

Answer 37

a high prevelance will increase the ppv and reduce NPV while a low prevelance will lower ppv and increase npv