Chapter 13

Question 1

Explain the difference between descriptive and inferential statistics. What is the purpose of each type?

Answer 1

Descriptive statistics simply summarize and describe the characteristics of a data set, while inferential statistics use data from a sample to make predictions or draw conclusions about a larger population; essentially, descriptive statistics tell you what is happening within your data, while inferential statistics allow you to generalize those findings to a broader group

Question 2

State the null hypothesis, and explain what this concept means. Then describe an example of a research study, and what the null hypothesis would be for that particular study

Answer 2

null hypothesisL assuming that the pop means r equal and any variation is due 2 random chance

Question 3

Under what conditions do we reject the null hypothesis? what are we saying about the data and variables in question when we do so?

Answer 3

when we find low prob that the diff between of occuring if pop means r equal

Question 4

What information is presented in a sampling distribution? How does this apply to the decision of whether to regret the null hypothesis?

Answer 4

• prob distribution of statistic obtained thru represented sampling of specific pop
• applies 2 decision of whether 2 accept null hypothesis
• shows how a particular sample will occur if null hypothesis is true

Question 5

Under what conditions do you use a one tailed t-test? What about a two-tailed t-test?

Answer 5

One-tailed test:
Used when you have a specific direction in mind for the difference (e.g., “Treatment A will result in higher scores than Treatment B”).
Only examines one tail of the distribution (either the upper or lower tail).

Two-tailed test:
Used when you are interested in any significant difference, regardless of direction (e.g., “Treatment A will differ from Treatment B”).
Examines both tails of the distribution (upper and lower).

Question 6

Explain the difference between system variance and error variance. What does each one have to do with achieving statisticall significance results/

Answer 6

systematic: deviation of group means from grandmean aka mean score of all individuals) error variance: deviation of individuals from mean of their respective group

Question 7

What is meant by a Type 1 Error? What is a Type II error? Offer a research scenario, and state what the Type I error would be as well as the type II error

Answer 7

type 1: reject null hypoth when it’s true (false pos)

type 2: accept when it’s false (false neg)

Question 8

false positive

Answer 8

type 1 error

Question 9

false negative

Answer 9

type 2 error

Question 10

T test formula

Answer 10

group diff/within group variability

Question 11

Discuss possible reasons for not achieving significant results in an experiment

Answer 11

1. chose 2 stringent significant level
2. sample size 2 small
3. too small effect size

Question 12

what is a t test

Answer 12

A t-test is a statistical tool that tells you if the difference between two or more groups is statistically significant

Question 13

When to use a t test

Answer 13

Use a t test to determine if two groups are
significantly different from each other.
 Ex: Is the mean of the no-model group different than
the mean of the model group?

Question 14

The F test

Answer 14

analysis of variance, is like a t test,
except that you can also use it on a complex
experimental design (more than two levels of an IV, or
more than two IVs). In concept, it’s the same as a t test:

Basically, it’s the ratio of systematic variance (how
different are the group means from each other) and error
variance (how much do the individual scores in each group
differ from their group mean).
 If the group means are very different, and there isn’t much
variance within each group, that makes the F ratio larger.