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Health in Context > Biostastics 1 > Flashcards

Biostastics 1 Flashcards

1
Q

Why is it important to study statistics?

A

Statistics enables us to use information from a sample of people to better understand populations.

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2
Q

How does studying statistics benefit critical thinking and analytical skills?

A

Studying statistics helps develop critical thinking and analytical skills

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3
Q

Why is the ability to read and evaluate journal articles important for informed information use?

A

Being able to read and evaluate journal articles makes one an informed user of information.

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4
Q

What would a population be like if all its members were identical?

A

If all members of a population were identical, the population would be homogenous (no variations in characteristics).

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5
Q

What is the reality of population characteristics in terms of variation?

A

In reality, all populations are heterogenous, meaning they have significant variations among members.

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6
Q

Why is it necessary to observe many individuals in a population study?

A

It is necessary to observe many individuals to capture all possible characteristics of the population.

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7
Q

What does using data to make an inference (to say something) about a population involve?

A

Using data involves making an inference with confidence about a whole population based on the study of only a sample.

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8
Q

What is the population in a statistical study?

A

The population is the entire group that you want to talk about.

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9
Q

What is a sampling frame in the context of statistics?

A

A sampling frame is the eligible population that you intend to sample.

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10
Q

What is a sample in a statistical study?

A

A sample is the subset of the population that you actually observe in the data.

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11
Q

Why is it important to have a sample that represents the population well?

A

We want a sample that is a good representation of all the characteristics in the population to make accurate inferences.

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12
Q

What is a representative sample

A

A representative sample is a sample that has characteristics that
are similar to the overall population.

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13
Q

What is random error in statistics?

A

Random error is a statistical error that occurs when a selected sample does not represent the entire population.

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14
Q

What happens when the results found in a sample do not represent the entire population?

A

It indicates the presence of random error or sampling error.

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15
Q

Why is it called “random error” or “sampling error”?

A

It is called “random error” or “sampling error” because it assumes the error is just by chance due to using a sample instead of the whole population.

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16
Q

How can we reduce sampling error?

A

We can reduce sampling error by increasing the sample size.

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17
Q

What is another method to reduce sampling error besides increasing sample size?

A

Selecting a representative sample helps reduce sampling error.

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18
Q

Why don’t researchers use the entire population in a study?

A

Using the entire population, or conducting a census, is very seldom done in survey research due to its impracticality.

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19
Q

What is a census in the context of survey research?

A

A census is a study that includes data about every member of the defined target population.

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20
Q

What are the two main ways samples can be drawn?

A

Samples can be drawn using probability/random sampling or non-probability sampling.

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21
Q

What is probability/random sampling?

A

Probability/random sampling is when each member of the population has a known probability of being selected and selection is not controlled by the investigators.

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22
Q

What is non-probability sampling?

A

Non-probability sampling is when samples are chosen based on personal judgment or convenience.

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23
Q

How should researchers decide which sampling method to use?

A

Researchers should weigh what fits best for their research question, budget, and timeframe.

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24
Q

What is random sampling?

A

Random sampling is a method where each member of the population has an equal chance of being selected.

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25
Q

How does random sampling benefit the sampling process?

A

It removes conscious and unconscious sampling bias.

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26
Q

What does random sampling allow researchers to do in terms of population parameters?

A

It permits the estimation of population parameters, allowing for statistical inference.

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27
Q

How are units selected in random sampling?

A

Units are selected randomly, in an unpredictable manner with no pattern.

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28
Q

Who controls the sampling process in random sampling?

A

The researcher controls the sampling process but not who gets selected.

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29
Q

How does random sampling reduce researcher bias?

A

It reduces researcher bias in the selection process by ensuring the selection is random and not influenced by the researcher’s preferences.

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30
Q

Types of Random sampling

A
  1. simple random sampling
  2. systematic random sampling
  3. stratified random sampling
  4. cluster random sampling
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31
Q

What is the first step in simple random sampling?

A

Assign each member of the population a number.

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32
Q

What is the second step in simple random sampling?

A

Decide on the sample size (n) that you need.

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33
Q

How do you select the members in simple random sampling?

A

Use a hat, random number table, or random number generator to select your “n” members.

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34
Q

If the population is 10,000 and the sample size is 400, what is the likelihood of being selected?

A

The likelihood of being selected is 400/10,000, which is 0.04 or 4%.
(each member of the population has an equal chance of being selected)

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35
Q

What is a key benefit of simple random sampling?

A

Each member of the population has an equal chance of being selected, leading to an accurate representation of the population.

36
Q

What is another advantage of simple random sampling?

A

It is easy to use.

37
Q

What is a disadvantage of simple random sampling if the sampling frame is large?

A

This method can be impractical if the sampling frame is large.

38
Q

What is a potential drawback regarding minority subgroups in simple random sampling?

A

Minority subgroups of interest in a population may not be present in the sample.

39
Q

What is the first step in systematic random sampling?

A

Assign each member of the population a number.

40
Q

What is the second step in systematic random sampling?

A

Decide on the sample size (n) that you want or need.

41
Q

What is the third step in systematic random sampling?

A

Calculate the selection interval.

42
Q

How do you determine the starting point in systematic random sampling?

A

Randomly select a starting point within the first interval using a random number table or random number generator.

43
Q

What do you do after selecting the starting point in systematic random sampling?

A

Include every nth member until the sample size is achieved.

44
Q

How does systematic random sampling compare to simple random sampling in terms of results?

A

It empirically provides identical results to simple random sampling but is more efficient.

45
Q

What is a stratum in stratified random sampling?

A

A stratum is a segment/sub-group of the population that shares at least one common characteristic (e.g., birth years, gender).

46
Q

How are characteristics distributed within and across strata in stratified random sampling?

A

The characteristic will be homogeneous within each stratum but heterogeneous across the strata.

47
Q

Can you give an example of strata in a population?

A

One group could consist only of women, and another group could consist only of men.

48
Q

Why would we use stratified random sampling if sub-groups may differ with regard to the measurement being made?

A

To ensure these sub-groups are adequately represented in the final sample

49
Q

What is the main advantage of stratified random sampling?

A

It helps ensure we sample a representative sample of the population.

50
Q

How are participants sampled within each stratum in stratified random sampling?

A

Participants are sampled from within each stratum using simple or systematic random sampling

50
Q

What is proportional stratified sampling?

A

Proportional stratified sampling is when the sample size in each stratum is proportional to the size of that stratum in the population

51
Q

In a population of 1,000 people, if 20% are current smokers, 30% are previous smokers, and 50% are never smokers, what would a proportional sample of 100 people look like?

A

A proportional sample would include:

20 current smokers
30 previous smokers
50 never smokers

52
Q

What is disproportional stratified sampling?

A

the sample size in each stratum is NOT proportion to the stratum size in the population

53
Q

What is a cluster in cluster random sampling?

A

A naturally occurring structure with a mixed aggregate of members of the population, with each member appearing in only one cluster.

54
Q

Can you give examples of clusters?

A

Schools, suburbs, hospitals, or clinics

55
Q

How does cluster random sampling differ from stratified sampling?

A

In cluster sampling, there is homogeneity across clusters but heterogeneity within each cluster, whereas stratified sampling has homogeneity within strata and heterogeneity between strata.

56
Q

What does it mean that clusters all look like each other?

A

All clusters are similar in type (e.g., all are primary care clinics), but the members within each cluster are as diverse as the population as a whole.

57
Q

Why is only a subset of clusters needed to represent the population?

A

Because each cluster is a microcosm of the entire population, representing its diversity

58
Q

When is cluster random sampling an efficient strategy?

A

When the population is spread over a large geographic area.

59
Q

What is the first step in cluster random sampling?

A

Develop or obtain a list of clusters.

60
Q

What is the second step in cluster random sampling?

A

Draw a random sample of clusters.

61
Q

What are the two options for sampling within selected clusters?

A
  1. Include everyone in each selected cluster.
  2. Draw a random sample of people from within each selected cluster.
62
Q

What is the desired internal relationship in stratified sampling?

A

Subjects in the same stratum are similar to one another regarding the stratifying factor (homogeneous).

63
Q

What is the desired external relationship in stratified sampling?

A

Each stratum is different from other strata.

64
Q

How are subjects included in the sample in stratified sampling?

A

All strata are represented in the sample.

65
Q

What is the desired internal relationship in cluster sampling?

A

Subjects in the same cluster are different from one another regarding the factor of interest (heterogeneous).

66
Q

What is the desired external relationship in cluster sampling?

A

Each cluster is similar to other clusters.

67
Q

How are subjects included in the sample in cluster sampling?

A

Only a subset of clusters are in the sample.

68
Q

Types of non- random sampling methods

A
  1. Convenience sampling
  2. Purposive sampling
  3. Snowball sampling
  4. Volunteer sampling
69
Q

What is convenience sampling?

A

Selecting participants who are close at hand, readily available, or convenient.

70
Q

What is purposive sampling?

A

Researchers select participants because they have specific characteristics of interest.

71
Q

When is purposive sampling often used?

A

It is often used in qualitative research when researchers are particularly interested in insights from certain types of people.

72
Q

What is snowball sampling?

A

Starting with one or two eligible participants and then asking them to refer others to participate in the study

73
Q

When is snowball sampling useful?

A

It is useful for accessing difficult-to-reach or hidden populations.

74
Q

What is volunteer sampling?

A

Participants self-select to participate in the study, often in response to an advertisement or call for participants.

75
Q

Is non-random sampling generalizable to the broader population?

A

No, non-random sampling methods are not generally considered to be representative of the broader population.

76
Q

When might non-random sampling methods be the only option?

A

Depending on the research question and population of interest, non-random sampling methods may be the only feasible option

77
Q

Why is estimating sample size important?

A

Estimating sample size ensures that the study has enough statistical power to detect meaningful and important results.

78
Q

What are the consequences of having too small a sample size?

A

Too small a sample size may result in inconclusive or imprecise results because meaningful effects may not be detected.

79
Q

What are the consequences of having too large a sample size?

A

Too large a sample size can lead to detecting statistically significant differences that are not practically important, wasting resources.

80
Q

What factors should be considered when determining sample size?

A

Feasibility factors such as time, cost, and availability of participants should be considered, although they should not dictate sample size.

81
Q

What to consider before calculating?

A

1.
Consider the research question
and study design
2.
Planned sampling strategy
3.
Consider the type of outcome measure
4.
What is the required level of precision
?
*
What is your acceptable margin of error? How much error can be tolerated?
5.
What level of confidence
do you want to use?
*95% is typical but definitely notmandatory
6.
What are the expected population parameters
?
*Is there an existing survey in a similar population? What does the literature say? What can you expect to find?

*Do you need to be able to detect a 2% difference between two groups? Would a 5 or 10% difference be more meaningful in the real world?

82
Q

How is the minimum sample size estimated for a prevalence study?

A

The minimum sample size
𝑛
n for estimating a single proportion (prevalence) can be calculated using the formula

​

where:

𝑝
p is the anticipated population proportion (prevalence).
𝑑
d is the desired precision or margin of error.
𝑧
z is the critical value from the standard normal distribution corresponding to the desired confidence level (e.g.,
𝑧= 1.96
z=1.96 for a 95% confidence level).

83
Q

What does
𝑝
p represent in this formula?

A

𝑝
p represents the expected proportion of the population exhibiting the characteristic of interest (prevalence).

84
Q

What does
𝑑
d represent in this formula?

A

d represents the desired precision or margin of error around the estimated prevalence

85
Q

Why is z=1.96 used in the formula?

A

corresponds to the critical value at the 95% confidence level in the standard normal distribution, ensuring a 95% confidence interval around the estimated prevalence.

Health in Context (4 decks)

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