Please enable JavaScript.
Coggle requires JavaScript to display documents.
Chapter 8 Hypothesis Testing - a statistical method that uses sample data…
Chapter 8 Hypothesis Testing - a statistical method that uses sample data to evaluate a hypothesis about a population USE - to determine if the statement has a real effect on the population mean
HYPOTHESIS TESTS
Non-Directional (Two-Tailed) Test - the standard format; predicts an effect without specifying direction
-
FOUR-STEP, STANDARDIZED PROCEDURE
-
-
-
Reminders
-
A constant changes the mean but does not
change the shape of the population, nor does it change the standard deviation.
-
EFFECT SIZE
Significant or 'Statistically Significant' effect if the decision from the hypothesis test is to reject the H0
-
If the z-score is large enough to be in the critical region, we reject the null hypothesis
and conclude that there is a significant treatment effect.
-
Step 4: Make a Decision
Reject the Null Hypothesis (H0) if the test statistic is in the critical region; result is statistically significant.
-
-
Fail to Reject the Null Hypothesis (H0) if the test statistic is not in the critical region; no evidence for treatment effect; data do not provide strong evidence that the
null hypothesis is wrong
Type II Error or 'a false negative' is when you fail to reject the false null hypothesis. Means that the hypothesis
test has failed to detect a real treatment effect.
Beta (β) The sample data are
not in the critical region even though the treatment has an effect on the sample.
the standard error determined by the variability of scores and the number of scores in sample (n); standard deviation of the population divided by the square root of n
-
the larger the variability,
the lower the likelihood of finding a significant treatment effect.
Null Hypothesis (H0) - states there is no effect - no change or no difference or no relationship - in the population; zero-effect hypothesis.
-