What are the goals of hypothesis testing
Rule out chance (sampling error) as a plausible explanation for the results from a research study
What are the null and alterative hypotheses
H0 (null hypothesis)= The observed findings are due to random chance
H1= The observed findings cannot be explained by sampling error
What is the process of hypothesis testing
1: State one’s hypothesis about the population
2: Define the probability at which one thinks results indicate that there must be a true effect
3: Obtain and test sample from the population
4: Compare data with the hypothesis predictions
What factors influence a hypothesis test
The size of treatment effect and size of sample
What is alpha level
Establishes a criteria or “cut off” for deciding if the null hypothesis is correct
What are type one and type two errors
Type one errors are when the sample data indicate an effect when no effect actually exists and type two errors are when the hpothesis test does not indicate an effect but in reality an effect does exist
How is a p-value used in hypothesis testing
One will check statistical significance by seeing if their test scores indicate a p-value of less than their α level
What does a p-value measure
Probability of obtaining an effect at least as extreme as the one in one’s sample data, assuming the truth of the null hypothesis
Approximately how likely is one to commit a type one error when p=0.05
35%
Why is effect size calculated in addition to statistical significance
In order to measure the magnitidue of an effect
How does one calculate Cohen’s D
M-μ/σ
How does one interpret Cohen’s D
It is the mean difference in terms of standard deviation
What is the power of a hypothesis test
The probability that one will correctly reject the null hypothsis when there is actually an effect
What factors influence statistical power
Effect size, sample size, alpha level, and non-directional vs directional hypothesis
