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MED 106- RESEARCH METHODS AND ESSENTIAL MEDICAL STATISTICS > 12. SAMPLING, RANDOM ERRORS AND CONFIDENCE INTERVALS > Flashcards

12. SAMPLING, RANDOM ERRORS AND CONFIDENCE INTERVALS Flashcards

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USMLE® Step 1 flashcards
1
Q
  1. What is Statistical Inference?
A
  • it is when we look at a specific aspect of a sample
    group
  • we then use these results to make assumptions about
    the total population
  • it is when the sample estimate is used to draw
    conclusions about the population
  • from which the sample was taken
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2
Q
  1. What is a sample?
A
  • it is a selected subset of a source population
  • the sample should ideally be representative of the
    source population
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3
Q
  1. Provide a brief definition of the Source Population?
A
  • this is the group of all the individuals that we are
    interested in
  • we use this group to assess certain parameters
  • it is the group that we want to make a statistical
    inference about
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4
Q
  1. What is the purpose of taking a sample?
A
  • it allows us to study a parameter that we cannot study
    in the whole population
  • this is because there are practical restrictions to
    studying the whole population
  • EG: time, money, resources
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5
Q
  1. How is scientific research almost always conducted?
A
  • through the use of samples
  • research may be conducted in whole populations as
    well
  • these populations are usually very small
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6
Q
  1. What is the process involved when it comes to Sampling?
A
  • there is a number of individuals that are selected
  • these individuals all come from the same source
    population
  • a sampling frame is necessary to do this
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7
Q
  1. What is a sampling frame?
A
  • it is a list or a database
  • it contains all the individuals in a population
  • it is used for sampling
  • sometimes this method cannot be used
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8
Q
  1. What are the Sampling Units?
A
  • they are the individuals that have the potential to be
    selected
  • these are individual people most of the time
  • there can also be larger sampling units
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9
Q
  1. List 4 examples of larger sampling units.
A
  • families
  • streets
  • hospitals
  • schools
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10
Q
  1. What kind of populations can the Source Population be?
A
  • it can be the general population
  • EG: total population of a city or country
  • it can also be specific sub-populations
  • EG: all smokers in a country
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11
Q
  1. What is Descriptive Research?
A
  • it is a research field in which we investigate the
    prevalence and incident rate of a condition in a
    population
  • there is a high focus on frequencies
  • EG: prevalence of Covid in Cyprus
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12
Q
  1. What is of high importance when it comes to sampling in Descriptive Research?
A
  • the sample has to accurately represent the specific
    source population
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13
Q
  1. What is Analytical Research?
A
  • this is when we investigate the association between
    exposure and outcome
  • EG: the association between obesity and diabetes
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14
Q
  1. What can be said about the source population obtained during Analytical Research?
A
  • the source population can be more general
  • this depends on the research question of interest
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15
Q
  1. What can be sad about the source population in situations where we are investigating a biological effect on some disease?
A
  • the source population we are identifying can be more
    general
  • this population is not necessarily restricted to a specific
    region or country
  • EG:
  • the effect of smoking on cancer
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16
Q
  1. What can be sad about the source population in situations where we are investigating a social or cultural effect on some disease?
A
  • we have to be more careful with the source population
  • we have to restrict it to a specific country or region
    from where the sample was derived
  • EG:
  • the effect of social class on the risk of heart disease
17
Q
  1. How do we determine the proportion of a characteristic in a population?
A
  • we measure it in a sample
  • we are measuring the estimate
  • this estimate carries an inherent error
  • this is known as a sampling error
18
Q
  1. What does Statistical Inference involve?
A
  • it involves the use of statistics
  • these determine the degree of uncertainty in the
    estimate of interest
19
Q
  1. Provide a definition for the term “estimate”.
A
  • is the proportion of the parameter in a sample
  • it is the measurement of a quantity (association) in a
    sample
  • it aims to represent the true quantity in the source
    population
20
Q
  1. What is a Parameter?
A
  • it is the measurement of a quantity (association) in a
    population
  • we are interested in this quantity
21
Q
  1. Provide 4 examples of Parameters.
A
  1. Mean Age
  2. Prevalence of Obesity
  3. Mean difference in blood pressure between men and
    women
  4. Odds Ratio for the association between smoking and
    cancer
22
Q
  1. What does the sample estimate attempt to quanitfy?
A
  • it attempts to quantify the corresponding population
    parameter
  • we want to see how close the sample estimate will be
    to the population parameter
23
Q
  1. What is the Sampling Error?
A
  • it is the difference in magnitude
  • between the sample estimates and the actual
    population parameter

THIS IS CAUSED BY:
- measuring a quantity (association) in a sample
rather than in the source population

24
Q
  1. What is Sampling Variation?
A
  • this happens when we take numerous different
    samples
  • we measure the same aspect from the same source
    population
  • it is the differences (variation) between the sample
    estimates
25
Q
  1. Why can we refer to Sampling Errors as “Random” or “Statistical” Errors?
A
  • this is because the sampling errors are a result of
    chance
26
Q
  1. What does the Sample Size play an important role in?
A
  • it plays an important role in the magnitude of the
    random error

NB:
- a smaller sample size leads to a larger sampling error
- the two are inversely proportional
- the sample variation will also increase
- the two are inversely proportional

27
Q
  1. Name all the measurements in which the Sample Size is inversely proportional to both the Sampling Error and the Sample Variation.
A
  • Incidence
  • Risk Ratio
  • Rate Ratio
  • Mean Difference
  • Correlation Coefficient
  • Regression Coefficient

NB:
- all of the above as termed “Estimates”
when they are calculated in a Sample

28
Q
  1. What is the Standard Error (SE)?
A
  • this describes the uncertainty of how well the sample
    estimate represents the population represents the
    population parameter
29
Q
  1. What does the Standard Error estimate?
A
  • it estimates the standard deviation of the sampling
    distribution
  • EG:
  • the average error that can occur whenever we take a
    sample of a certain size (n)
30
Q
  1. For what quantities does the sStandard Error exist?
A
  • it exists for all statistical quantities
31
Q
  1. From which value can the Standard Error be estimated from?
A
  • it can be estimated from a single sample
32
Q
  1. How do we calculate the Standard Error with regards to the mean?
A

S= sample standard deviation
n = sample size

NB:
- you do not have to know this formula

33
Q
  1. What is the relationship between the Sample Size and the Standard Error?
A
  • they are inversely proportional
  • an increase in the sample size leads to a decrease in
    the standard error
  • a decrease in the sample size leads to an increase in
    the standard error
34
Q
  1. What can we use the Standard Error to calculate?
A
  • it allows us to calculate the degree of uncertainty
    around an estimate
  • this is known as the 95% Confidence Interval
35
Q
  1. What does the Confidence Interval indicate?
A
  • it indicates a range (interval)
  • we are confident that the true population parameter
    lies within this range
  • we do still have some uncertainties
36
Q
  1. How do we calculate the 95% Confidence Level?
    (95% Cl)
A
  • we work out the Lower Confidence Interval
  • we work out the Upper Confidence Interval

LOWER CONFIDENCE INTERVAL:
- Sample Estimate MINUS 1.96 x Standard Error Value

UPPER CONFIDENCE INTERVAL:
- Sample Estimate PLUS 1.96 x Standard Error Value

37
Q
  1. How do we write the Interpretation for the 95% Confidence Interval?
A
  • we are 95% Confident that the Population Parameter is
    contained within the Interval Sample Estimate
  • This interval if found from the Lower Confidence
    Interval to the Upper Confidence Interval
38
Q
  1. What happens when we increase the Sample Size?
A
  • we decrease the Confidence Interval
  • the estimate becomes more precise

MED 106- RESEARCH METHODS AND ESSENTIAL MEDICAL STATISTICS (43 decks)

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