381 Quiz 7 Study

Front 1

abstract, high level construals

Back 1

capture the overarching meaning and central, superordinate features of an object or event

Front 2

concrete, low level construals

Back 2

consist of the immediate experience and specific, incidental features of an object or event

Front 3

begin your presentation in the abstract ad then

Back 3

gradually and systematically focus on concrete details

Front 4

pacing

Back 4

audience members should be treated as blank slates but quick studies

must be crafted so that it inspires others with the research story based on the presenter's own detached appreciation for the most important, compelling elements of the story

Front 5

In shaping the presentation of your data, should you share all of this output in order to demonstrate your team's statistical prowess? Should you walk your listeners through every analysis you conducted so they feel as if they sat with you as you crunched the numbers?

Back 5

no and no

Front 6

the most important segment of the research talk conveys your team's own

Back 6

hypotheses, methods and results

Front 7

know the editing as a team order on page 173

Back 7

know the editing as a team order on page 173

Front 8

2 steps of stats:

Back 8

1. look at the data
2. infer something from the data

Front 9

descriptive statistics examples

Back 9

Use statistics such as frequencies, measures of central tendency, and/or variability
Mean, median, mode
Range, variance, standard deviation

Front 10

inferential statistics definition

Back 10

Attempt to infer something about the population from the study’s sample

What are the chances that differences between means (or an apparent relationship between variables) are due to sampling error?

Front 11

why we can't prove anything

Back 11

relying on probabilities

Front 12

p value meaning example

Back 12

then there is a 14% chance that we would see this pattern of data if there is really no difference between groups

if p = .14

Front 13

Type 1 error

Back 13

we reject the null hypothesis but the null hypothesis was actually true

For the criminal justice system, Americans find type II errors disturbing but not as horrifying as type I errors. A type I error means that not only has an innocent person been sent to jail but the truly guilty person has gone free. In a sense, a type I error in a trial is twice as bad as a type II error. Needless to say, the American justice system puts a lot of emphasis on avoiding type I errors.  This emphasis on avoiding type I errors, however, is not true in all cases where statistical hypothesis testing is done.

Front 14

Type 2 error

Back 14

accept null hypothesis but null hypothesis actually false
...not finding effects that are actually there

In statistical hypothesis testing used for quality control in manufacturing, the type II error is considered worse than a type I. Here the null hypothesis indicates that the product satisfies the customer's specifications. If the null hypothesis is rejected for a batch of product, it cannot be sold to the customer. Rejecting a good batch by mistake--a type I error--is a very expensive error but not as expensive as failing to reject a bad batch of product--a type II error--and shipping it to a customer. This can result in losing the customer and tarnishing the company's reputation.

Front 15

power depends on (2)

Back 15

sample size and effect size

Front 16

chi square

Back 16

Tells us the differences in the frequencies of categories

Front 17

t tests

Back 17

A t-test is any statistical hypothesis test in which the test statistic follows a Student's t distribution if the null hypothesis is supported. It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic (under certain conditions) follows a Student's t distribution.

one categorical, one continuous variable

Front 18

correlations use....

Back 18

two continuous variables

Front 19

ANOVA is an ____ test

Back 19

omnibus

Front 20

histogram

Back 20

a graphical representation showing a visual impression of the distribution of data. It is an estimate of the probability distribution of a continuous variable and was first introduced by Karl Pearson.[1] A histogram consists of tabular frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to the frequency of the observations in the interval. The height of a rectangle is also equal to the frequency density of the interval, i.e., the frequency divided by the width of the interval. The total area of the histogram is equal to the number of data. A histogram may also be normalized displaying relative frequencies. It then shows the proportion of cases that fall into each of several categories, with the total area equaling 1.