descriptive statistics
process of describing data.
most often performed on a small subset of a population, known as a sample
inferential
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allows researchers to draw conclusions on the population-based on information collected from the sample.
builds on descriptive statistics
1-sample Z-Test
Testing whether sample mean is equal to a target value when "population variance is known"
Independent variable: 1 known sample
Dependent variable: 1 continuous variable
Tested Value: mean
Sample size GREATER THAN 30
1 sample t-test
Tests whether the mean of a single population is equal to a target value
Independent variable: 1 known sample
Dependent variable: 1 continuous variable
Tested Value: mean
Sample size LESS THAN 30
1-sample Z test for dichotomous variable
Independent variable: 1 sample (comparing against a known value, a population parameter)
Dependent variable: 1 dichotomous variable
Tested Value: Proportion
Sample size GREATER than 10 for each category of the dichotomous variable
1 sample t test for dichotomous variable
Independent variable: 1 known sample
Dependent variable: 1 dichotomous variable
Tested Value: proportion
Sample size LESS than 30
2 sample z test
Assumptions:
1. Samples are independent of each other
2. Individuals/observations in each sample are independent
3. Both samples are sufficiently large (less than 10%)
Conditions:
1. Think about how data were collected
2. SRS ("A simple random sample (SRS) of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected.")
3. Sample is less than 10% of population for BOTH samples
4. Successes & failures are greater than or equal to 10
Independent variable: 2 groups/2 treatment groups
Dependent variable: 1 continuous variable
Tested value: means
Sample size GREATER than 30
important beginning point for hypothesis testing
1-way ANOVA
Independent variable: 3 or more different groups (if nominal variable), or 3 more different treatment or exposure levels (if an ordinal variable)
Dependent variable: 1 continuous variable
Tested value: means
Will not tell you which of the alternate hypotheses you are rejecting the null hypothesis in favor of. Further analysis needed
Chi square test of goodness of fit
Independent variable: 2 different groups (a dichotomous variable); 3 or more different groups (if a nominal variable); or 3 or more different treatment or exposure levels (if an ordinal variable)
Dependent variable: 1 categorical variable (nominal or ordinal)
Tested value: proportions or cell counts
Null hypothesis: The data are consistent with a specified or expected distribution
Alternate hypothesis: The data are not consistent with a specified or expected distribution
chi-square test of independence
Determines whether two variables are independent or related; the test can be used with nominal or ordinal data.
Independent variable: 1 categorical variable (nominal or ordinal)
Dependent variable: 1 categorical variable (nominal or ordinal)
Tested value: proportions or cell counts
Null hypothesis: in the sample, the 2 categorical variables are independent
Alternate hypothesis: in the sample, the 2 categorical variables are not independent
Mann-Whitney U test
a nonparametric version of a t-test
Determines whether two uncorrelated means differ significantly when data are nonparametric
Independent variable: 2 different groups (a dichotomous variable; e.g., men and women); 2 treatment groups (hypertension and normal blood pressure groups)
Dependent variable: a continuous variable; data not normally distributed
Tested value: U statistic (based on the ranks of the data points)
Null hypothesis: the populations are equa [Show Less]