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113 Cards in this Set
- Front
- Back
Incidence rate
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It's a measure of the risk. Number of new events/number of persons exposed to risk
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Prevalence rate
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It's a measure of the extent. All cases of a disease/total population at risk
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Relationship between incidence and prevalence
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Prevalence = Incidence X Duration
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What happens to incidence and prevalence if: New effective treatment is initiated
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Prevalence decreases
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What happens to incidence and prevalence if: New effective vaccine gains widespread use
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Both incidence and prevalence decrease
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What happens to incidence and prevalence if: Number of persons dying from the condition increases
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Prevalence decreases
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What happens to incidence and prevalence if: Additional research funds are added
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No change in either incidence nor prevalence
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What happens to incidence and prevalence if: Behavioral risk factors are reduced in the poulation
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Both incidence and prevalence decrease
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What happens to incidence and prevalence if: Contacts between infected and noninfected persons are reduced
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Both incidence and prevalence decrease
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What happens to incidence and prevalence if: Recovery from the disease is more rapid
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Prevalence decreases
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What happens to incidence and prevalence if: Long-term survival rates for the disease increase
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Prevalence increases
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Morbidity rate
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Rate of disease in a population at risk. Both incident and prevalent cases.
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Mortality rate
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Rate of death in a population at risk. Incident cases only.
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Attack rate
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A type of incidence in which the denominator is further reduced for some known exposure
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Point prevalence
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Prevalence at a specified point in time
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Period prevalence
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Prevalence during a span of time
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Crude rate
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Measured rate for whole population
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Specific rate
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Measured rate for a subgroup of the population
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Standardized rate
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Adjustment to make groups equal on some factor
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Number needed to treat
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Inverse of incidence rate. Means that I would have to treat X number of people to prevent one case. 1/ARR; ARR = event rate in control group - event rate in treated group
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Crude mortality rate
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Deaths/population
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Cause-specific mortality rate
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Deaths from cause/population
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Cause-fatality rate
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Deaths from cause/number of people with the disease
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Proportionate mortality rate (PMR)
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Deaths from cause/all deaths
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Sensitivity
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The percentage of sick people for whom the test was positive: TP / TP + FN or a/a+c or 1-FN rate
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False negative rate
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1 - sensitivity
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Specificity
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The percentage of healthy people identified as not having the disease: TN / TN + FP or d/(d+b) or 1-FN rate
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False positive rate
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1 - specificity
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Positive predictive value
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The probability that a person with a positive test truly has the disease: TP / TP + FP or a/(a+b)
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Negative predictive value
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The probability that a person with a negative test doesn’t have the disease: TN / TN + FN
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Accuracy
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TP + TN / total screened patients
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What is the relationship between positive and negative predictive values and prevalence
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Prevalence is directly proportional to PPP and inversely proportional to NPP
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Selective bias
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The sample is not representative of the population
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Measurement bias
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The information is gathered in a manner that distorts the information.
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Berkson bias
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Selection bias in which hospital records are used to estimate population prevalence
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Nonrespondent bias
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Selection bias in which people included in the study are different than those who are not
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Hawthorne effect
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Subject's behavior is altered because they are being studied. Only a factor when there's no control group in a prospective study.
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Solution to selection bias
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Use a random, independent sample.
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Solution to measurement bias
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Set up a control/placebo group
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Experimenter expectancy bias
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Experimenter's expectations are passed on to subjects producing the desired effects.
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Solution to experimenter expectancy bias
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Double-blind design - neither the experimenter nor the subject know who receives the intervention.
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Lead-time bias
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Gives a false estimate of survival rates. Confuses improved screening with improved survival.
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Solution to lead-time survival
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Measure back-end survival - measure increased life-expectancy
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Recall bias
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Subjects fail to accurately recall events in the past. It's a problem in retrospective studies.
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Solution to recall bias
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Use multiple sources to confirm information
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Late-look bias
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Individuals with severe disease are less likely to be uncovered in a survey because they die first
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Solution to late-look bias
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Stratify by severity.
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Confounding bias
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Factor being examined is related or influenced by other factors of less interest
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Solution to confounding
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Do multiple studies and good research design
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Case report
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Clinical characteristic or outcome from a single clinical subject or event
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Case series report
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Clinical characteristic or outcome from a group of clinical subjects. Just diseased, no control group.
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Cross-sectional study
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The presence or absence of disease and other variables in a representative sample at a particular time. Measures prevalence, not incidence. Cause and effect cannot be determined.
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Case-control study
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People with disease compared to a control group. Almost always retrospective. Doesn't measure incidence or prevalence but determines causality. Qualities of the healthy are compared to qualities of the sick, determines risk factors. Use odds ratio.
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Cohort study
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Group with risk factor is compared to group without it - prospective. Oppossite of case-control. Measure incidence in each group, determines causality. Most reliable and valid. Use relative risk or attributable risk
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Tools used to analyze cohort studies and incidence data
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Relative risk and attributable risk
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Relative risk
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Incidence rate of exposed group / incidence rate of the unexposed group. Greater chance of one group of disease compared to the other group. Used for cohort studies.
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Attributable risk
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Incidence rate of exposed group - incidence rate of unexposed group. How many more cases in one group. Used for cohort studies.
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Odds ratio
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AD/BC; where A is the table cell of the object of study and D is diagonally across from it. Chance of risk given disease. Used for case-control studies.
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Observational studies
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Case, case series, cross-sectional, case-control, cohort
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Phase 1 clinical trial
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Testing safety of drug in healthy volunteers
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Phase 2 clinical trial
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Testing protocol and dose levels in small group of patient volunteers
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Phase 3 clinical trial
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Efficacy and occurrence of side effects in large group of patient volunteers.
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Intervention studies
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Randomized controlled clinical trial, community trial, cross-over study
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Randomized controlled clinical trial
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Subjects are randomly allocated into intervention and control groups. Most rigorous study. Double-blind is when neither patients nor doctors know which group a patient is in. Least subject to bias, expensive.
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Community trial
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Entire community is tested
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Cross-over study
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All subjects receive intervention, but at different times.
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Combine probabilities for independent events
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By multiplication
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Combine probabilities for nonindependent events
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Multiply the probability of one event by the probability of the second, assuming the first event occurred
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Combine probabilities for mutually exclusive events
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By addition
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Combine probabilities for events that are not mutually exclusive
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Add the two probabilities and subtract the multiplied probabilities
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Central tendency values
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Mean, median, mode
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Mean
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Average = sum of the values / number of values
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Median
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The 50th percentile. The value which divides the set into an upper half and a lower half.
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Mode
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The most frequent value encountered
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Positive skew of the distribution curve
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Tail is to the right, mean greater than median
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Negative skew of the distribution curve
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Tail is to the left, median is greater than mean
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Best central tendency measure for skewed distributions
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Median
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Best central tendency measure for normal distribution
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Mean, median and mode are all the same
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1 standard deviation
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68% of cases
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2 standard deviations
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95.5% of cases
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3 standard deviations
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99.7% of cases
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Between the mean and 1 standard deviation
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34% of cases
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Between 1 standard deviation and 2 standard deviations
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13.5% of cases
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Between 2 standard deviations and 3 standard deviations
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2.4%of cases
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Above 3 standard deviations
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0.15% of cases
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Confidence interval
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A percentage that assures how much up or down from the sample the true population is.
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95% confidence
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Z = 2
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99% confidence
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Z = 2.5
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Confidence interval
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Mean +- Z (S/square root of the sample size)
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Confidence interval for relative risk and odds ratio
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If the CI range excludes 1 then it is significant. If the range is above one --> increased risk; if the range is below one --> decreased risk. If the CI range includes 1, then it is not significant
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Null hypothesis
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The opposite of what is trying to prove. E.g. hypothesis: the drug works; null hypothesis: the drug doesn’t work
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p-value < 0.05
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Reject the null hypothesis - reached statistical significance
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p-value > 0.05
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Do not reject null hypothesis - has not reached statistical significance
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Type I error or alpha error
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Rejecting the null hypothesis when it's really true - asserting the drug works, when it really doesn’t. The p-value is the chance of a type I error - if p=0.05, then chance of type I error is 5%.
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Type II error or beta error
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Failing to reject the null hypothesis when its really false - asserting the drug doesn’t work, when it does. Cannot be estimated from p-value.
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Statistical power
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1 - P = beta error
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How to increase power
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Increase the sample size, which increases power and decreases type II errors
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Types of scales in statistics
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Nominal, ordinal, interval, ratio
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Nominal scale
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Puts objects into different groups or categories. Gender, drug Vs. placebo group, etc…
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Ordinal scale
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Puts groups into sequence, ranks or in different states of quality. Olympic medals, class rank, etc…
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Interval scale
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A group that is ordered in such a way that we can tell not just that they're different in quality but in quanity as well (how much do they differ). Height, weight, blood pressure, drug dosage, etc.
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Ratio scale
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Like interval scale but has a true zero point below which it cant go. Kelvin temperature scale, etc…
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Pearson correlation
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All interval data
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Chi square
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All nominal data
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t-test
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2 groups with interval and nominal data
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ANOVA
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more than 2 groups with nominal and interval data
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All interval data - which statistical test?
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Pearson correlation
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All nominal data - which statistical test?
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Chi-square
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Combined interval and nominal data - which statistical test?
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If two groups: t-test; if more than two groups: ANOVA
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Meta analysis
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Statistical combination of the results of many studies, yielding a single p-value that represents the sum of all.
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What is the range of correlation analysis values?
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minus 1 to plus 1
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What can be infered from a correlation analysis value of -1?
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Strong negative correlation - the variables are inversely proportional. Scatterplot shows bunched up dots with a negative slope.
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What can be infered from a correlation analysis value of +1?
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Strong positive correlation - the variables are directly porportional. Scatterplot shows bunched up dots with a positive slope.
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