Data, Evidence and Decisions

Question 1

Data is?

Answer 1

Numerical values, percentages and decimals

Question 2

Evidence is?

Answer 2

Background information i.e. Theory of a subject

A ‘bridge’ to a conclusion

Question 3

When does Data become evidence?

Answer 3

Data becomes evidence when it is used to test a hypothesis

Question 4

What are the two data types?

Answer 4

Qualitative: Non numerical judgements e.g. what kinds of dogs

Quantitative: Numerical e.g. how many dogs

Question 5

Thematic Analysis, 1st step?

Answer 5

1. Make meaning units

Summarise lines of interviews

Question 6

Thematic Analysis, 2nd step?

Answer 6

2. Condensed meaning units

Further summarise into simpler lines or words

Question 7

Thematic Analysis, 3rd step?

Answer 7

3. Codes

Make codes which are thematic elements repeated in an interview

Question 8

Thematic Analysis, 4th step?

Answer 8

4. Categorise

Further categorise these codes

Question 9

Thematic Analysis, 5th step?

Answer 9

5. Identify a theme

Identify an underlying pattern in all these interviews

Question 10

Accuracy is?

Answer 10

How close your results are the true value

Improved with: Better tools

Question 11

Precision is?

Answer 11

How close the results are to each other

Improved with: Better tools, same conditions
Damaged by: Random events

Question 12

Reliability is?

Answer 12

The ability for the experiment’s results to be replicated = PRECISION

Internal: Repeating results in the same experiment
External: Repeated results by another team

Question 13

Validity is?

Answer 13

Whether you’re finding the answer to the question properly

Internal: Is the experiment sound?
External: Another team sees if it is sound i.e. meta analysis

Question 14

CRAAP stands for?

Answer 14

Currency
Relevance
Accuracy: 
Authority
Purpose

Question 15

Types of measurement errors and how can they be fixed?

Answer 15

Systematic: parallax, calibration, limit reading (affects accuracy)
Random: random stuff happening (affects reliability and precision)

Repeat tha tests

Question 16

What is the purpose of statistical analysis?

Answer 16

To find an underlying pattern and draw inferences from a population

Question 17

What is a random sample population and why are they used as opposed to the total population?

Answer 17

Random sample population: A portion of the population which generally reflects the pattern in all of a population

They are used for the sake of cost effectiveness since they (should) reflective

Question 18

A good sample has what three qualities?

Answer 18

• Large enough
• All done under the same condition
• Follow population pattern

ALL REMOVE BIASES

Question 19

What do error bars show?

Answer 19

Variability in range and or standard deviation

Question 20

When is mean difference significant and not significant?

Answer 20

If standard deviation&raquo_space; (meanA - meanB) = not sig

If standard deviation &laquo_space;(meanA - meanB) = sig

Question 21

What are the two types of bias?

Answer 21

Measurement error

Sampling bias

Question 22

Define 95% confidence interval

Answer 22

Data recorded there will be an area of data (confidence interval) with 95% overlap

Question 23

Significance 0.05 is?

Answer 23

95% chance the thing being investigated is the reason for difference in data and 5% chance is random

Question 24

Data cleansing

Answer 24

Removal or irrelevant stuff and errors and correction of incorrect data points

Question 25

T-test, 1st step?

Answer 25

Find the t-value (calculated and given)

Question 26

T-test, 2nd step?

Answer 26

Compare to respective critical value (in the degrees of freedom row, 0.05 column)

Question 27

T-test, 3rd step?

Answer 27

If t-value > critical, mean difference is significant reject null If t-value > critical, mean difference due to chance (accept null)

Question 28

T-test, 4th step?

Answer 28

Look for p-value, smaller p is better

Question 29

When are t-tests used?

Answer 29

For comparing two means