Though Questions Unit 2

Psychology 210 with Hendricks at University of Wisconsin - Madison

About this deck

By: Robyn Armon
Textbook:

Statistics for the Behavioral Sciences
Created: 2010-03-22
Size: 45 flashcards
Views: 241

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1. What is the difference between a population, sample, and samplING distribution (think about conceptual differences ? what they are composed of and what they are used for ? and the symbols that are used for each)?

Population - uses greek letters - entirety of what your studying-variability=sigma, center is ?
Sample - subset of population, usually representative of the population - english letters-center = M, varib =s
sampling distribution - distribution of sample means, variab=?_M. mean=?, infer from samples about population

2. What is the difference between an empirically derived and a population derived (theoretical) sampling distribution? What are advantages and disadvantages of each?

Empirically derived is trial and error. take repeated samples of a population and find all the means. then find the mean of those means. --CON-time consuming
Population derived - based on CLT1 -CON- need to know population

3. What does the Central Limit Theorem (CLT) suggest regarding the characteristics of the sampling distribution of the mean?

normalcy increases as n increases

variabilty = ?_M

mean = ?

4. What impact does sample size have on our sampling distributions? As we increase our sample size, what happens to sampling error? What is the standard deviation of a sampling distribution called?

the bigger the sample size the more clustered around the, the narrower it is.
sampling error decreases
?_M

5. Earlier we described the sample M as an unbiased estimator of ?. How is that fact illustrated by, or related to, the CLT?

the sampling distribution is centered around and has a mean is ?

6. What is the relationship between the shape of the population and the shape of the sampling distribution? Why is this important?

There isn't one. they are independent of each other

7. Be able to give some practical applications of the sampling distribution. How might it be of practical use? To what kind of situations does it apply?

mean age of jurors
battery life
any problem relating M's

8. Be able to explain how the CLT permits us to estimate the position of m if we only know sample information? Be able to explain my ?reverse logic? for this estimation. Why is it that we can take info from one sample and infer things about the population?

only 5/100 times the ? will not fall with the intervals set by the means
can infer things about population through the samples
without the CLT we can not predict/set probability

9. What is the difference between the scientific and statistical hypotheses? Explain what is represented by the null and alternate hypotheses? Be able to generate examples of each. Specify attributes of each. Why do we focus so much on the null hypothesis for hypothesis testing?

Scien:what you want to find --alt hypoth
Stat: what you want to disprove -null hypoth
focus on null because easier to prove some thing wrong once than prove it right for every possible case

10. Explain the difference between directional and non-directional hypotheses. What are the implications of using a one-tail vs. two-tail hypothesis test? When should each be used?

directional-one tailed-shows an increase or decrease in the treatment--better power
non directional--two tailed--more conservative and safer--when in doubt use this --shows a change(significance)but not which direction

11. What is meant by a Type I error (be sure that you can describe it in practical terms, for a real investigation, not just referring to the null hypothesis)?

reject null when you shouldn't have
claim significance when there is none
purely happens by chance--experimenter has no control over it
represented by alpha

12. If ? = .05, what does that mean (in practical terms)? Why don?t we set alpha even lower?

the lower alpha, the higher beta, the lower power. = bad
there is a 5% chance of committing a type 1 error
means found in that region are considered significant and not due to chance

13. Why do scientists want to minimize Type I errors? Think about potential costs associated with a Type I error?

can create false hope for patients
money
integrity
reputation for science and scientist
time--delays
may not consider treatments that actually work

1. What is meant by a Type II error (again think in practical terms)? What are some factors that influence the likelihood of this error?

claim no significance when there actually was significance
represented by beta
fail to reject the null
Factors:
---sample size
---diff to be detected
---variabilty
---test type (directional? dependent?)

15. Explain the way(s) in which the experimenter influences or has control over Type I and Type II errors (direct & indirect).

type 1-experimenter sets alpha level

type 2--sample size choice, directionality chosen, dependence chosen

16. What is the difference between Type I and Type II errors vs. biases (malfeasance) in the conduct of research?

type 1 happens purely buy chance, the experimenter has no control and did nothing wrong
type 2- experimenter choses sample size and the other factors listed previously and therefore has more blame in committing of a type 2 error

17. Explain the connection between sampling distributions (the CLT) and hypothesis testing.

without the CLT hypothesis testing would not be possible the sampling distribution is made and set around the mean which is used in the null and alternate hypothesis of hypothesis testing

18. What is meant by ?statistical significance?? What is meant by ?p < .05?? This is a probability of what? Under what circumstances do we make such statements?

the event is not due to chance, what happened took plae because of the treatment or testing done. there is <5% chance of committing a type 1 error

tobs>tcrit

19. What is a confidence interval, and what does it reveal? What is it centered around? How is it related to hypothesis testing? How is it different?

� Says that 95% of the sample means will fall within a given confidence int.?the ? will fall w/in this as well

� Both hypoth and conf int based on alpha level?foundation material is similar

� Hypoth testing based on null hypoth, conf int based on actual outsomes (range)

� Suggested as an alt to hypoth testing

� Follow up to 2 tailed

20. What is the difference between a point estimate and an interval estimate? Discuss their usefulness.

� Point estimate is a mean

� Interval estimate is confidence int

21. Be able to describe Cohen?s effect size. What is meant, in practical terms, for a small, medium, or large effect size?

� In std dev units

� Shows how much a treatment actually effected a condition

� Measure of magnitude

22. What does an effect size contribute that significance does not? Is it possible to derive significance, but have a weak effect size? When might this be more likely?

� Significance only tells if something had an effect or not, effect size tells you how much of an effect it actually had

� Easier to have significance but small effect size with large n

23. What is meant by power? Why is it important? Be sure to understand our power diagram.

� Ability to detect a change in the data if such a change actually occurred

� Power = 1 ? ?

� Can see if a treatment will be effective before testing is actually done

� Power increases as type 2 error decreases

24. Why must a specific alternative hypothesis be identified to calculate power? How does this hypothesis differ from our original hypotheses?

� Tells you what change you are looking for you, if you do not specify a specifc hypoth you could be looking at a infinite # curves

25. Be able to demonstrate the impact of several variables (e.g., a, n, directional/non-directional tests, discrepancy to be detected, variability) on power and b. Think about what each does to our power diagram.

� Sample size increase power increase?width shrinks

� Direction -> non directional?alpha decreases, beta increases, power decreases

� Discrepancy to be detected- position of alt curve changes?difficult small differences

� Alpha changes- decreases, beta increases, power decreases?goal line changes

� Variability ? position shifts, variability increases, power decreases

26. What are some criticisms of, or concerns regarding, traditional hypothesis testing?

� Arbitrary significance?not based on actual consequences of type 1 error

� Dichotomous logic--black and white!

� Overemphasis on significance

� Inadequate attention to other factors that influence significance- i.e. sample size, variance(poor control)

27. What alternatives to traditional hypothesis testing are available? How do they differ (assumptions, interpretation, etc.)?

� Confidence interval?alpha level still arbitrary

� Alpha level based on risk ?what constitutes risk?

� Report actual probability and reader can determine significance

28. How does the sampling distribution of the mean change when sigma is unknown?

� When sigma unknown use s. there is more variability and with s therefore curve widens, and t tests are used

29. What is the difference between the t and z distributions? Explain why the t distribution needs to be wider than that for z?

z is normal.

t approaches normal with increases sample size

t has more variability and is therefore wider

with small n, s is unreliable

30. How is the shape of the t distribution affected by df? Explain why.

as df increases t widens

31. We calculated the ?proportion of variance accounted for? by the IV. What does this mean? What does it reveal about our experiment that isn?t revealed by significance testing?

what percentage of the total variance is accounted for by the treatment/study
measure of magnitude in percent
how much influence th IV had on the DV

32. There are multiple ways of evaluating the outcome from an investigation. Compare the interpretation of significance, confidence interval, effect size, power, and proportion of variance accounted for.

� Confidence int:---The actual data found, 95% of the sample means should fall within this interval?still based on alpha (CLT)?not dichotomous like hypoth testing

� Hypoth. Testing focuses on the null (what didn?t happen)

o -black and white-no gray.

� Effect size---how many std dev did the treatment chance the control

� Proportion of variance tells how much of the total variance can be accounted for by the study done

33. How does our sampling distribution change when we use two groups in hypothesis testing? Think about a revised CLT (call it CLT2), describing key characteristics of this sampling distribution.

the center is around mean₁ -mean₂, which is 0. the variability is _{s_m1-m2.}-normal distribution(approaching even more normal with increasing n
made of differences between means
very robust

34. What is meant by independent groups designs (a.k.a., between subjects designs or between groups designs)? Give examples.

not matched or used repeated measures

not family, social pairs, pre/post test, ect

35. Sometimes we have equal n in two groups that are being compared and sometimes not. What impact does this have on our calculations? What is meant by a ?pooled standard error?, and why is that necessary?

when equal n errors can just be averaged together, when unequal they are weighted the one wit the bigger n as more weight than the the other

� Homogeneity of variance-if the n?s are not close enough it is not safe to continue with the tests

36. What is meant by homogeneity of variance (homoscedasticity)? How can we test to see whether our data meets this assumption? How rigid (or lenient) is the F-max test in evaluating this assumption, e.g., do the variances have to be exactly the same? How does SPSS evaluate this assumption?

fmax and levine(SPSS)

fmax is very robust in that is allows much deviation from the rules

s's are close enough to continue

37. What assumptions are behind independent groups tests? What is meant if we say that a given test is robust? How does this relate to the assumptions for the independent groups test?

robust regaring normality of curve

roughly equal n's

38. What is meant by dependent groups? Give examples of research designs appropriately analyzed with these techniques.

paired, matched, repeated measures

39. How is the sampling distribution altered for dependent groups vs. independent groups (perhaps CLT3)? Compare the size of the standard error that one would typically obtain for dependent vs. independent groups.

everything remains the same except sampling error which decreases because the consistent ind diff are accounted for and subtracted out

40. I suggested two different ways of calculating the standard error for dependent groups. Explain how both procedures account for consistent individual differences in the data?

� Correlation equation subtracts out consistent indiv differenced right away

� S_d/?n uses the individual differences variation right away

41. How does the correlation between sets of scores influence the outcome for dependent groups? What does this suggest regarding the use of matching variables?

� It is multiplies by the error that gets subtracted out therefore the better the correlation the lower the error.

� The better groups are matched the better the correlation the lower the error

42. How is power affected by using independent vs. dependent groups? Discuss the impact of the correlation on power.

� Power is greater for dependent because there is less error

� Based on degrees of correlation- r increases power increases

43. Consider the pros and cons for using dependent groups for hypothesis testing.

� PROS ? better power, more correlation, less error, subtract out individual differences

� CONS ? difficulties in matching, carryover effects, loss of extremes

44. What difficulties are encountered in estimating the required sample size for a given study? What kinds of information are required before you can estimate the appropriate sample size?

� Need a lot of information prior to study ?need alpha, beta (power if possible) variability in data, type of test, 1/2 sample directional? In/dependent, difference to be detected

45. How does specifying a desired effect size help in estimating sample size? How is this considered a short cut, i.e., what information is no longer required by using the effect size?

� Don?t need to know variability or difference to be detected�need to know to know power, so good that we don?t have to know them

� Estimate ratio of cohens d?what effect size do you want?

About this deck

By: Robyn Armon
Textbook:

Statistics for the Behavioral Sciences
Created: 2010-03-22
Size: 45 flashcards
Views: 241

About StudyBlue

“Simply amazing. The flash cards are smooth, there are many different types of studying tools, and there is a great search engine. I praise you on the awesomeness.”
Dennis