chap 9 Flashcards
How Z Scores are Used in Research
Knowing the probability that a z score will occur helps determine how extreme a z score to expect before determining that a factor other than chance produced the outcome
Hypothesis testing
procedure deciding whether the outcome of a study support a particular theory or innovation at the population levelIs is real or due to chance?
ex: Home Field Advantage
Null Hypothesis (H0)There is no difference between the numbers of wins at home and the numbers of wins away.Research Hypothesis (H1)Teams will win more at home, than they will win away.
ex: Is the coin that I’m flipping fair?
Null Hypothesis (H0)There will be = amount of flips for each sideResearch Hypothesis (H1)More flips on 1 side than they on the otherSuppose you flip heads 10 times in a row. Is the coin biased?
What does it mean for a result to be “significant”?
You can reasonably conclude thatresults you are due to a systematic influence and not to chance alone.
Confidence in our Results
If we eliminate pure chance as the reason we see a difference in our groups / reason we see evidence to reject the NH, the only thing left to account for what we see is the factors we are studying
Significance Level
The risk associated with not being 100% confident that what you observe in an experiment is due to the treatment/what is being tested or studied.
Type error I
Claim there is an effect/difference when one does not exist in the population. (Falsely reject the NH) “False Positive”Gps r actually = something falsely leads you to believe they are notGreek letter Alpha
Type II error
Claim there is no effect/difference when 1 exists in the population. Fail to reject the NH when you should. (Your research hypothesis is true!)False NegativeGps/conditions r actually different, but your evidence doesn’t support this. Greek letter Beta
Choosing a Significance Level
On any one test of the NH there is a 5% chance you will reject it when the NH is actually true. (Claim there is a gp difference when there isn’t).The probability of observing this outcome in the “normal population” is less than .05. (In this case, the “Outcome” is rejecting the NH when it is true) There is a 5% probability that a score is that extreme if the NH is true
Type I and Type II Errors, A Balancing Act
best to minimize both.If significance level at .000001 to control for a Type I error, you risk being too stringent to detect a real effect – committing a Type II error.Tradeoffs must be made
Significance vs. Meaningfulness
Significance is a mathematical term referring to probability. Meaningfulness is determined by looking at the context of the findings.
How Inference Works
representative sample of pop. is chosen.test is given mean r computed & comparedA conclusion is reached as to whether the scores are statistically significant (Different)Based on results of the sample, an inference is made about the population.
Test of significance
A statement of the NH.Set the level of risk associated with the NH3. Select the appropriate test statistic.4. Compute the test statistic obtained value5. Determine value needed 2 reject the NH using the appropriate table of critical values6. Compare obtained value to critical value7. If obtained value more extreme reject NH8. If obtained value is not more extreme, accept the NH
Making Inferences from the Sample to the Population
With the normal curve, we r assuming the pop. mean is 0. What if we conduct a study, and the mean of our study is -2 SD away from 0?1 – we were wrong about NH. Population mean is not 0. Reject Null. 2 – random variation is forcing our sample mean to be different from 0. Accept Null.
