introduction + sampling design Flashcards

(29 cards)

1
Q

what are statistics?

A

the collecting and analysing of numerical data to test a sample.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

what are the 2 types of statistics?

A

quantitative and categorical

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

what is quantitative statistics?

A

the measure in quantity, numerical values that represent different magnitudes of variable.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

what is categorical statistics?

A

each observation belongs to one set of categories.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

what do the graphs look like in a categorical set of data?

A

more ordered than quantitative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

what do the graphs look like in a quantitative set of results?

A

dotted around

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

what are the 2 parts of quantitative statistics?

A

discrete and continuous

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

what is discrete statistics?

A

values from a set of separate numbers

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

what is continuous statistics?

A

possible values from an interval

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

what are the 2 parts to quantitative statistics?

A

nominal and ordinal

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

what is nominal statistics?

A

the values arranged in groups

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

what is ordinal statistics?

A

ordered in ranked data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

what is descriptive statistics?

A

to describe and understand the property of your data.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

what does descriptive statistics tell you about?

A

central tendency (mean, median etc), and the measurement of variability

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

what is inferential statistics?

A

methods to test research of hypothesis

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

why do you need statistics?

A

to see trends in data, to check likelihood, to make research quantifiable, verifiable, defensible etc.

17
Q

what do you have to do in a scientific method process?

A

make an observation, define a question, develop a hypothesis, design an experiment, collect data, analysis of data, develop theory, if reject- go back to designing the experiment.

18
Q

what is an alternative hypothesis?

A

clearly stated proposition which should be tested and falsifiable

19
Q

what is the null hypothesis?

A

statement which is accepted or rejected in favour of the alternative

20
Q

what are the aims for study design?

A

examine how changes in one or more treatments can change. does x affect y etc.

21
Q

what is an experimental study?

A

manipulation of treatment of interest under controlled conditions, random

22
Q

what is an observational study?

A

observations of different levels under interest. occurs in the natural sites using natural controls.

23
Q

what do you need more than one treatment or control field?

A

need for replication, the world is noisy and hence there are lots of other factors which your aren’t controlling.

24
Q

why is random sampling good?

A

enables you to quantify how much of an effect there is, and how likely it is to be real. its not biased, and the maths behind it is fair

25
what is simple random statistics?
random selection from the total population
26
what is stratified random statistics?
splitting up your population according to treatment, and then randomly selecting within each "strata"
27
what is important about the independence of samples?
no sample should be influenced by another sample.
28
what is pseudoreplication?
when individual samples are heavily dependent on one another.
29
what does pseudoreplication cause?
means that your sample size is smaller than what you think you have.