Distributions
This describes the phenomenon that approximately 68% of the data falls within 1 standard deviation of the mean, 95% falls within 2 standard deviations, and 99.7% falls within 3 standard deviations.
Empirical Rule
The amount of options a claim about a single population parameter in hypothesis testing can have.
6 options
Critical values are
The boundary lines or the number of standard errors away from the center of the distribution based on the level of confidence
This is a confidence interval.
A range of values used to estimate the true value of a population parameter
The degrees of freedom are calculated using
df = n - 1
This happens when the standard deviation of a normal distribution decreases.
The curve becomes taller and narrower
A statement about a population parameter that claims equality and is always assumed to be true.
Null Hypothesis (Ho)
This happens to the critical values when the level of confidence increases.
The critical values will increase or move further into the tails of the distribution
This equation computes the minimum sample size for a confidence interval using standard deviation.
n = ((Critical Value * Standard Deviation)/ Error)^2
This is what you should do when the p-value is less than the level of significance.
Reject the null hypothesis
This is the Excel formula used to calculate probability given a z-score.
NORM.S.DIST
Not equal to, less than, greater than or different than are used in this hypothesis.
Alternative Hypothesis (Ha)
This happens to the critical values as the level of confidence decreases.
The critical values will decrease or move closer to the center of the distribution
This computes confidence intervals for parameters.
Add/subtract the margin of error to/from the sample mean
This is the level of confidence that should be used if there is not one given in the problem.
95%
In samples of this size, the sample means will be normally distributed, regardless of the shape of the sample.
30 or larger
A hypothesis test claiming a null hypothesis less than or equal to a value is of this nature.
One-tailed right
This is when you use the Standard Normal Distribution to determine Z- critical values.
You use z-critical values for inference of the population mean when the population standard deviation is known
This equation computes the margin of error for confidence intervals.
Margin of Error = Critical Value * Standard Error
This is computed as the complement of beta (probability of type II error).
Power of the test
This is the formula to calculate standard error of the proportion, also known as
SEp = sqrt [p(1 - p)/n], standard deviation of the sample proportion
(True or False) If the sample statistic does not provide enough evidence to refute the claim, we accept the null hypothesis.
False
To determine T- critical values, the sample standard deviation is used when
You use t-critical values for inference of the population mean when the population standard deviation is unknown
This formula computes Z critical values for confidence intervals.
NORM.S.INV(alpha/2) negative z scores, NORM.S.INV(1-(alpha/2)) positive z scores
This occurs when you incorrectly accept a false null hypothesis.
Type II Error