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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
what makes a sample random
unbiased collection of a sample - equal and independent chance of being selected for a sample
26
what are some difficulties for a random sample being equal chance
- environmental affects (whether some units/individuals are easier to be chosen than others) - sample of connivence
27
what are some difficulties for a random sample being independent
samples of connivence (samples taken from one location samples taken close together)
28
how to take random samples
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
methods for getting random numbers
- roll dice - flip coin - random number generator
30
how can you NOT get random numbers
by thinking of the number yourself (self conscious patterns)
31
what is a sample of convenience
collection of easily available individuals
32
why are samples of convenience NOT desirable
- leads to bias - might not reflect truthful over the whole population
33
what is volunteer bias
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
variables vs data
variables - characteristics that differ among individuals data - measurements of one or more variables made on a sample of individuals
35
two types of variables
- categorical - numerical
36
categorical variables
describe membership in a group (sort samples into different groups) based on qualitative analysis of individuals
37
categorical variable examples
eye colour height (short, medium tall) age group (young, old) blood type morphological traits (spots, strips)
38
two types of categorical variables
nominal and ordinal
39
compare the two types of categorical variables
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
numerical variables
measurements that are quantitative (have magnitude)
41
examples of numerical variables
height (cm) age (years) weight (g or kg) number of trichomes per leaf
42
two types of numerical variables
continuous and discrete
43
compare the two types of numerical variables
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
explanatory vs response variables
explanatory the variable that is manipulated by the researcher response the measured effect or outcome of the experiment
45
experimental or response variable: independent variable
experimental variable
46
experimental or response variable: dependent variable
response
47
estimates
related quantity calculated from a sample
48
can the selection of one member of the population affect another in random sampling
NO
49
how is bias shown in a set of samples
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
benefit of random sampling
minimizes the bias and makes it possible to measure the amount of sampling errors
51
frequency
the number of observations having a particular value of the measurement
52
frequency distribution
how often each value of the variable occurs in the same
53
what is frequency distribution used for
to inform about the distribution of the variable in the population it came from
54
probability distribution
distribution of a variable in the whole population
55
normal distribution
approximates the distribution of a variable in the population from where a sample came from
56
confounding variables
variable that masks or distorts the causal relationship between measured variables in a study
57
do two events being associated together raise or lower the probability of one being the cause of the other
raises it
58
how can variables correlate WITHOUT one being the cause of the other
result from a common cause
59
how do confounding variables affect studies
by giving misleading or false relationships between the measured variables
60
experimental artifacts
when bias in a measurement is produced unintentionally through experimental procedures
61
experimental studies vs observational studies
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 (9 decks)

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