# Though Questions Unit 2

- StudyBlue
- Wisconsin
- University of Wisconsin - Madison
- Psychology
- Psychology 210
- Hendricks
- Though Questions Unit 2

**Created:**2010-03-22

**Last Modified:**2011-07-02

#### Related Textbooks:

Statistics for the Behavioral Sciences__ING__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 μ
- Sample - subset of population, usually representative of the population - english letters-center = M, varib =s
- sampling distribution - distribution of sample means, variab=σ
_{M}. mean=μ, infer from samples about population

- 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

_{M}

_{}mean = μ

- the bigger the sample size the more clustered around the, the narrower it is.
- sampling error decreases
- σ
_{M}

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

__one__sample and infer things about the population?

- only 5/100 times the μ 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

- 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

- 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

- 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

- 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

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

- 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?)

- 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

· Says that 95% of the sample means will fall within a given confidence int.—the μ 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· Interval estimate is confidence int

· In std dev units

· Shows how much a treatment actually effected a condition

· Measure of magnitude· 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· Ability to detect a change in the data if such a change actually occurred

· Power = 1 – β

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

· Power increases as type 2 error decreases· 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 decreases26. 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)·
Confidence interval—*alpha level still arbitrary*

·
Alpha level based on risk –*what constitutes risk?*

- 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

· 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

- the center is around mean
_{1}-mean_{2}, which is 0. the variability is_{sm1-m2. }-normal distribution(approaching even more normal with increasing n - made of differences between means
- very robust

- 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

· Correlation equation subtracts out consistent indiv differenced right away

· S_{d}/√n uses the individual differences variation right away

· 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

· Power is greater for dependent because there is less error

· Based on degrees of correlation- r increases power increases· PROS – better power, more correlation, less error, subtract out individual differences

· CONS – difficulties in matching, carryover effects, loss of extremes

· Don’t need to know variability or difference to be detectedàneed 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?#### Words From Our Students

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