Final review tips for the AP Statistics exam
1) Know your vocabulary!!!
2) Know your formulas!!!!
3) Know where to find the different tests using your
TI-83, TI-84 or Casio calculator
4) Know the difference in describing a sample as large
versus small and what this means when selecting the
formulas.
5) When the observed value is greater than the critical
value - REJECT THE NULL
6) When the p - value is less than the alpha value -
REJECT THE NULL
7) Remember we never accept the alternative -
REJECT THE NULL
When we reject the null the statement should read:
We have sufficient evidence to doubt that the _____
is ______.
8) When stating hypothesis, remember that the null
contains the condition of equality, the alternative
hypothesis does NOT!!!!
9) When asked to describe a one-variable data set,
always discuss shape, center, and spread.
10) Understand how skew-ness can be used to
differentiate between the mean and the median.
11) Know how transformations of a data set affect
summary statistics.
12) Be careful when using normal as an adjective.
Normal refers to a specific distribution, not the
general shape of a graph of a data set. It’s better
to use approximately normal, mound shaped,
bell-shaped instead. You will be docked for saying
something like “the shape of the data set is
normal”. Also remember that when you do not hav
the standard deviation the sample or population are
said to be unpooled versus pooled, which is when
you have the standard deviation of the population.
13) Remember that a correlation does not necessarily
imply a real association between two variables.
Remember correlation is not causation.
Conversely, the absence of a strong correlation
does not mean there is no relationship (it might not
be linear).
14) Be able to use a residual plot to help determine if a
linear model for a data set is appropriate. Be able
to explain your reasoning.
15) Be able to determine in context, the slope,
y-intercept, of a least squares regression line.
16) Be able to read and interpret a computer regression
output.
17) Know the definition of: simple random sample SRS.
18) Know what blinding and confounding variables are:
19) Understand the meaning of Simpson's Paradox
20) Know the explanation behind Hawthorne's effect
21) Understand the aspects of the Rosenthal effect and
its impact on expected outcomes.
22) Know the difference between randomization and
blocking.
23) Know how to create a simulation for a probability
problem.
24) Be clear on the distinction between independent
events and mutually exclusive events.
25) Know why can’t mutually exclusive events be
independent.
26) Be able to find the mean and standard deviation of
a discrete random variable.
27) Recognize binomial and geometric situations.
28) Never forget hypothesis are always about
parameters, never about statistics.
29) Any inference procedure involves four steps.
30) Know the difference between type I & type II
errors.
31) Know how to construct a confidence interval and
interpret what a confidence interval means.
32) Be aware of the scale used in creating graphs.
33) Know the difference between descriptive versus
inferential statistics.
34) Know the meaning of statistics.
35) Quantitative versus qualitative statistics.
36) Know the process involved when collecting data.
37) Know your 5 number summary and how it relates to
the box plot.
What you need to know about one-variable data analysis
38) Shape of a distribution
39) Dotplot
40) Stemplot
41) Histogram
42) Measures of center
43) Measures of spread
44) 5-number summary
45) boxplot
46) z-score
47) density curve
48) normal distribution
49) the empirical rule
50) Chebyshev’s rule
What you need to know about two-variable data analysis
51) Scatterplots
52) Lines of best fit
53) The correlation coefficient
54) Least squares regression line
55) Coefficient of determination
56) Residuals
57) Outliers and influential points
58) Transformation to achieve linearity
You need to know about the design of a study, sampling, surveys, and experiments
59) Samples and sampling
60) Surveys
61) Sampling bias
62) Experiments and observational studies
63) Statistical significance
64) Completely randomized design
65) Blocking
What you need to know about random variables and probability
66) Probability
67) Random variables
68) Discrete random variables
69) Continuous random variables
70) Probability distributions
71) Normal probability
72) Simulation
73) Transforming and combining random variables
What you need to know about binomial distributions, geometric distributions, and sampling distributions
74) Binomial distribution
75) Normal approximation to the binomial
76) Geometric distribution
77) Sampling distribution
78) Central limit theorem
What you need to know about confidence interval and inference
79) Estimation
80) Confidence intervals
81) t-procedures
82) choosing a sample size for a confidence interval
83) p-value
84) statistical significance
85) hypothesis testing procedure
86) errors in hypothesis testing
87) power of test (not likely on the AP exam)
What you need to know about inference for means and proportions
88) The logic of hypothesis testing
89) Z - procedures and t procedures
90) Inference for a population mean
91) Inference for the difference between two population
means
92) Inference for a population proportion
93) Inference for the difference between two population
proportions
What you need to know about the inference for regression
94) Simple linear regression
95) Significance test for the slope of a regression line
96) Confidence interval for the slope of a regression line
97) Interference for regression using technology
What you need to know for categorical data: chi-square
These are called non-parametric tests meaning that the shape of the population is irrelevant.
98) Chi-square goodness of fit test
99) Chi-square test for independence
100)Chi-square test for homogeneity of proportions
(populations)
101) X^2 versus z^2
