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25 Cards in this Set
- Front
- Back
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Prevalence Rate
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Number of cases of a disease present in a population divided by the total number of people at risk in the population at a particular time; stated per 100,000 people
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Incidence Rate
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number of new cases of a disease occurring in the population divided by the total number of people at risk of developing the disease per unit of time; stated per 100,000 people
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Mortality Rate
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total number of deaths from all causes divided by total number of people at risk per unit of time
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Attack Rate
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ratio of the number of people contracting a particular disease to the total number of people at risk x100; useful in deducing the source of an epidemic; look at the attack rate table
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68-95-99.7 Rule
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68% of the area under the normal curve lies within 1 std dev from the mean (either + or -); 95% --> 2 std devs; 99.7 --> 3 std devs
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Statistical Inference (population vs sample)
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a sample is taken from the population to create data; this data gives statistics about the sample; infer that these statistics represent the population; inference about population from statistics about sample is called a parameter
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Central Limit Theorem
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a sample can be normally distributed even when the population is not
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Standard Error
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used to calculate the Z-score
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Procedure for Hypothesis Testing
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state null and alt hypothesis; choose level of significance (alpha); determine sample size and collect data; calculate z or t score (t for small n and if only have sample information [no pop]; z for large n); use table to determine if z score falls within acceptance region; accept or reject null hypothesis (never reject Ha)
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P-value
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small p value provides good evidence against Ho
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Significance Levels
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p >0.05: not significant
0.01< p < 0.05: significant 0.001< p <0.01: highly significant p <0.001: very highly significant if significant, reject Ho if not significant, accept Ho |
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Type I Error
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Ho is true but reject it; like Christ
alpha |
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Type II Error
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Ho is false and accept it: like OJ
beta |
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Point Estimate
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provides a single estimate of the parameter (population)
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Interval Estimate
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provides a range of values that seeks to capture the parameter (confidence interval); if interval crosses 0, it is not significant
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Confidence Levels
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90% --> Z = 1.645
95% --> Z = 1.96 99% --> Z = 2.576 |
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Determining Which Hypothesis Test to Use
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one sample: z if std dev is known, t if not known
two samples: paired t if have paired samples, independent t if have two independent samples k independent samples-ANOVA for one-, two- or multi-way |
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Calculate Confidence Interval
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CI = sample mean +/- (z-score)(std error)
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Summary of Probabilities: A or B
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P(AUB) = P(A) + P(B) - P(A and B)
if A and B are mutually exclusive, don't subtract overlap |
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Summary of Probabilities: A and B
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P(A and B) = P(A given B) x P(B)
= P(A) x P(B) if A and B are independent |
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Cohort Study
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based on data directly from patient
prospective study relative risk |
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Case Controlled Study
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based on data directly from patient
retrospective study odds ratio |
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Relative Risk Association
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= 0 --> no association with disease
>1 --> + association <1 --> - association |
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Odds Ratio Association
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>1 --> + association with disease
=1 --> no association <1 --> - association |
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F statistic
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mean square between groups divided by mean square within groups
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