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Biostatistics > Samples and Sampling > Flashcards

Samples and Sampling Flashcards

1
Q

why are statistics important

A
  1. to analyze data and draw conclusions
  2. quantify uncertainty
  3. making predictions
  4. assessing evidence
  5. sampling populations
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2
Q

what are two goals of statistics

A

estimation and hypothesis testing

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3
Q

parameters

A

quantities describing populations being studied

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4
Q

estimates relate to

A

samples

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5
Q

how are estimates and parameters linked

A

inferring a parameter is done through the use of estimates

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6
Q

examples of parameters

A
  • averages
  • numbers (size of pop)
  • variants (spread of data)
  • proportions (precent something is true)
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7
Q

how is a null hypothesis used in hypothesis testing

A

start with a null hypothesis stating/assuming there is no difference or effect regarding the testable quantity of a population and through the tests either support or reject the relationship

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8
Q

how are estimates and hypothesis testing related

A

your estimate is what is used for the hypothesis testing

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9
Q

what are reliable population estimates dependent on

A

a good sampling practice

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10
Q

what kind of samples are most desirable for science/ stats

A

random samples

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11
Q

why are random samples wanted for stats

A

limits possibility of bias

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12
Q

what is a population

A

the entire group/individual units being studied that are too large to measure individually

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13
Q

examples of populations

A
  • all cats falling from buildings in a city
  • all fish in a lake
  • all genes in a genome
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14
Q

what is a sample

A

selection of the subset of a population used to draw conclusions that ideally apply to the whole population

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15
Q

are samples smaller or larger than the population

A

smaller

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16
Q

examples of samples

A
  • cats taken to the vet (after falling from buildings)
  • random selection of fish in a lake
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17
Q

are sampling errors mistakes

A

NO - just differences between the estimate and the true value seen in the population

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18
Q

how will estimates differ from population characteristics

A

by random chance

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19
Q

is sampling error related to precision or accuracy

A

precision

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20
Q

high vs low sampling error

A

high
- estimates are more spread out = imprecise = high error

low
- estimates are close together = precise = low error

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21
Q

what defines an unbiased sample

A

when the average of estimates MATCHES the true population value

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22
Q

what is bias a symptom of

A

sampling problem

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23
Q

is bias related to precision or accuracy

A

accuracy

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24
Q

high vs low bias

A

high
- estimates may be close together but FAR from the true value = inaccurate = biased

low
- estimates may be close or far apart, but are average or even on the true value in the pop = accurate = unbiased

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25
Q

what makes a sample random

A

unbiased collection of a sample
- equal and independent chance of being selected for a sample

26
Q

what are some difficulties for a random sample being equal chance

A
  • environmental affects (whether some units/individuals are easier to be chosen than others)
  • sample of connivence
27
Q

what are some difficulties for a random sample being independent

A

samples of connivence

(samples taken from one location

samples taken close together)

28
Q

how to take random samples

A
  1. assign every individual with a random number between 1 and N (mas pop size)
  2. select random integers based on the sample size (n)
29
Q

methods for getting random numbers

A
  • roll dice
  • flip coin
  • random number generator
30
Q

how can you NOT get random numbers

A

by thinking of the number yourself (self conscious patterns)

31
Q

what is a sample of convenience

A

collection of easily available individuals

32
Q

why are samples of convenience NOT desirable

A
  • leads to bias
  • might not reflect truthful over the whole population
33
Q

what is volunteer bias

A

bias created based on how different people are more likely to volunteer than others for a study
(those that need money, those that are closer, those with time)

34
Q

variables vs data

A

variables - characteristics that differ among individuals

data - measurements of one or more variables made on a sample of individuals

35
Q

two types of variables

A
  • categorical
  • numerical
36
Q

categorical variables

A

describe membership in a group (sort samples into different groups) based on qualitative analysis of individuals

37
Q

categorical variable examples

A

eye colour

height (short, medium tall)

age group (young, old)

blood type

morphological traits (spots, strips)

38
Q

two types of categorical variables

A

nominal and ordinal

39
Q

compare the two types of categorical variables

A

nominal
- no ranking needed for the groups
(blood type, eye colour, morphological)

ordinal
- DO have a ranking for the groups

(height - short to tall NOT short, tall, medium

age- young, adult, old NOT old, young, adult)

40
Q

numerical variables

A

measurements that are quantitative (have magnitude)

41
Q

examples of numerical variables

A

height (cm)

age (years)

weight (g or kg)

number of trichomes per leaf

42
Q

two types of numerical variables

A

continuous and discrete

43
Q

compare the two types of numerical variables

A

continuous
variable can take on any value in a range
(height, age, weight)

discrete
variables can only have 1 value (counting) - they are integers

(number of trichomes in a leaf, petals on a flower, number of amino acids in a protein)

44
Q

explanatory vs response variables

A

explanatory
the variable that is manipulated by the researcher

response
the measured effect or outcome of the experiment

45
Q

experimental or response variable:
independent variable

A

experimental variable

46
Q

experimental or response variable:
dependent variable

A

response

47
Q

estimates

A

related quantity calculated from a sample

48
Q

can the selection of one member of the population affect another in random sampling

A

NO

49
Q

how is bias shown in a set of samples

A

sampling process would favour some outcomes over others which means the measurements on these samples would NOT be an accurate representation of the population

50
Q

benefit of random sampling

A

minimizes the bias and makes it possible to measure the amount of sampling errors

51
Q

frequency

A

the number of observations having a particular value of the measurement

52
Q

frequency distribution

A

how often each value of the variable occurs in the same

53
Q

what is frequency distribution used for

A

to inform about the distribution of the variable in the population it came from

54
Q

probability distribution

A

distribution of a variable in the whole population

55
Q

normal distribution

A

approximates the distribution of a variable in the population from where a sample came from

56
Q

confounding variables

A

variable that masks or distorts the causal relationship between measured variables in a study

57
Q

do two events being associated together raise or lower the probability of one being the cause of the other

A

raises it

58
Q

how can variables correlate WITHOUT one being the cause of the other

A

result from a common cause

59
Q

how do confounding variables affect studies

A

by giving misleading or false relationships between the measured variables

60
Q

experimental artifacts

A

when bias in a measurement is produced unintentionally through experimental procedures

61
Q

experimental studies vs observational studies

A

experimental studies:
- the researcher randomly assigns subjects to treatments

observational studies
- assigning subjects to treatments is NOT done by the researcher (like the cats falling from buildings in NYC)

Biostatistics (4 decks)

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