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Chapter 8: Hypothesis Testing - Coggle Diagram
Chapter 8: Hypothesis Testing
a statistical procedure that allows researchers to use sample data to draw inferences about the pop of interest
one of the most commonly used inferential procedures
combine concepts of z-scores, probability, and the distribution of sample means to create hypothesis testing
4 steps for hypothesis testing procedure: state hypotheses, set criteria for a decision, collect data & compute sample statistics, make a descision
used to evaluate the results of a study
use a sample mean to test a hypothesis about a pop mean
begin with a known population
goal is to determine what happens to the pop after the treatment is administered
can't use the treatment on the whole population so we take a sample
one basic assumption is made about the effect of the treatment
test statistic
z-score formula is like a recipe you follow
first and most important hypothesis is the null hypothesis
states that the treatment has no effect
H stands for hypothesis and zero indicates zero-effect hypothesis
reject or accept null which concludes if there is evidence that the treatment has an effect
the second hypothesis is the opposite of the null it is called
scientific, or alternative hypothesis (H1)
states that there is a change or difference
alpha level or level of significance is a probability value that is used to define the concept of "very unlikely" in a hypothesis test
critical region is composed of the extreme sample values that are very unlikely to be obtained if the null hypothesis is true
critical region consists of those sample values that provide evidence that the treatment has an effect
need to find the boundaries for the critical region
use the alpha-level probability and the unit normal table
alpha level for a hypothesis test is the probability that the test will lead to a Type I error
Type I error occurs when a researcher rejects the null that is actually true
could mean that the information from the sample is misleading because samples are different from their population
represented by greek letter
alpha
Type II errors occur when a researcher fails to reject a null that is in fact false
means that hypothesis test has failed to detect a real treatment effect
probability of a type II error is represented by greek letter
beta
statistically significant
z-score could influence hypothesis
so could the variability of the scores
number of scores in the sample
random sample and independent observations
directional hypothesis test or one-tailed test specify either an increase or a decrease in the population mean
critical region is defined by sample outcomes that are very unlikely to occur if the null is true
the
power
of a statistical test is the probability that the test will correctly reject a false null. The probability that the test will identify a treatment effect if one really exists
calculating the power: sketch the distribution for the null and alternative hypotheses, locate the critical region and compute, and compute the z-score for the alternative distribution and find power
other factors one-tailed vs two-tailed tests and alpha level
