chapter 5-Inferential Results-Why did the authors reach their conclusion-What did they actually find?

Question 1

what are inferential results

Answer 1

• Inferential results are results intended to explain or predict a variable or variables

Question 2

what do descriptive stat results allow us to know and explain. is this enough?

Answer 2

• Descriptive statistical results allow us to know and explain variables that we are interested in understanding but we have to go a step further to use that explanation to predict or infer how those variables may occur in the future. This can be done w inferential stats which are based ont he concepts of probability and statistical significance

Question 3

in inferential stats how do you test for relationsips, differences, assoc among variables

Answer 3

by creating distributions of test stats that reflect variables having no connection between them, are unrelated, or are not differen

Question 4

when do we say that a stat is statistically significant

Answer 4

• If a test stat falls out of the range of values that we would expect to occur 95% of the time, if there were no relationship among the variables

Question 5

what is t value

Answer 5

• The t value is a test stat for differences in means between two groups.
• A t distribution hows how the t tests for hundreds of different samples of two variables that didn’t differ from each other in the real world would be distributed (see image on pg 93 it looks like a bell curve)

in this image the green section is where the t-test values will fall 95% of the time if the two variables tested aren’t different.t he red zones on either end of the normal curve are the areas where the t values will fall by chance alone 2.5% of the time if, in the real world the two variables tested aren’t different

Question 6

what is p value

Answer 6

• P value represents probability

is defined as the percentage of the time the result found would have happened by chance alone.

So, if we infer from the finding of statistical significance that the difference, association, or relationship that we tested statistically is one that exists in the real world because we were unlikely to get our statistic by chance alone

Question 7

are inferential methods used for Qnt Qal or both

Answer 7

• Inferential methods only used for quantitative

Question 8

what is a confidence interval

does it usually include zero

Answer 8

• Confidence intervals state the range of actual values for the stat we are computing (eg difference in mean ages of students who do and do not choose acute care settings) in which 95 out of 100 values would fall

no doesnt include zero

Question 9

how are CIntervals and p values expressed differentaly

Answer 9

• CIs are almost always stated for the 95% range, whereas the probability of getting the result reported if theres really no difference or relationship is gen reported as one of 3 possible percentages: 5% (p

Question 10

what assumption is made p value vs CI

Answer 10

The relationship or difference tested is zero for p value

The relationship or ifference is that found in the data in CI

Question 11

meaning P value vs CI

Answer 11

p value Gives the % of the time that wed get the test statistic by chance alon

CI gIves the range of values (biggest and smallest numbers) that would occur 95% of the time for the relationship or difference found

Question 12

interpretation of p value vs CI

Answer 12

The smaller the p value the less likely that the test result occurred by chance alone

CI=The smaller the range, without zero in it, the more confident we can be that the test stat reflects the real world

Question 13

is statistical significance an absolute guarantee that the values are really different or related in the real world.

Answer 13

no
Stat significanc means that theres less than 5% chance that the amount of relationship or difference found happened by chance

Question 14

who do parametric and nonparametric studies refer to in gen

Answer 14

• These tests refer to the two broad classes of inferential ssts procedures that can be applied to numeric results from studies

Question 15

what 2 criteria are required before you can apply a parametric stat

Answer 15

 The numbers must gen be normally distributed (the freq distribution of the numbers is roughly bell-shaped and
 The numbers must be interval or ratio numbers, eg age or intelligence scores (the numbers must have an order, and there must be equal distance between each value

Question 16

what are nonparametric stats and some examples

Answer 16

• Nonparametric stats are used for numbers that don’t have a bell-shaped distribution and are categoric or ordinal (represent variables for which theres no est equal distance between each category, eg numbers used to represent gender or rating or preference for car color

Question 17

wuld gender and litigation be para or nonparametric

what about age and education in yrs

Answer 17

• In predictors of postconcussive symp, gender and litigation would be nonparametric and age and education in yrs scores would be parametric

Question 18

are t tests parametric or nonparametric

Answer 18

parametric

Question 19

• Researchers use different types of stats to test for the same kind of relationship depending on the form of data collected..

Answer 19

.

Question 20

for what 3 reasons do researchers use stat tests

Answer 20

o Look at differences between groups for on or more variables
o Look at relationships among two or more variables
o Look at relationships of factors within a variable itself

Question 21

what are multivariate vs bivariate tests

Answer 21

bivariate used when looking at two variables or two different groups

Question 22

from ppt: which kinds of tests Address Basic difference (Research question and hypothesis)

Answer 22

T-tests
One-Way Analysis of Variance (ANVOVA)
Repeated-Measures ANOVA
Pearson Chi-Square Test

Question 23

Address Basic Association and Correlational research questions and hypothesis

Answer 23

Pearson Product-Moment Correlation Coefficient (r)
Spearman Rank-Order Correlation Coefficient
Simple and Multiple Linear Regression

Question 24

ppt: which tests Address Basic Association and Correlational research questions and hypothesis

Answer 24

Pearson Product-Moment Correlation Coefficient (r)
Spearman Rank-Order Correlation Coefficient
Simple and Multiple Linear Regression

Question 25

are these tests for independent groups multivaiate or bivariate results ``` ANOVA –parametric ANCOVA, MANOVA, one way ANOVA-parametric Kruskal-Wallis one-way ANOVA- nonparametric Repeated measures ANOVA- parametric Chi-square for independent samples ```

Answer 25

multivariate

Question 26

are these groups used for related groups or independent groups and are they multi or bivariate * T test (parametric) * Sign test or median test (nonparametric) * Mann-Whitney U (nonparametric) * Wilcoxon rank test (nonparametric) * Fisher exact test (nonparametric)

Answer 26

independent and bivariate

Question 27

when looking at whether relationships between variables-have a natural connection between two or more variables what tests do you use

Answer 27

``` bivariate • Pearson r • Spearmon rho • Kendall tau • Contingency coefficient ``` ``` multivaiate Multiple regression (parametric) Canonical correlation (parametric) Path analysis (parametric) Structural equation modeling (parametric) Discriminant analysis (parametric) Logistic regression (nonparametric) ```

Question 28

if youre looking for a test for differences between two roups what to use what is ti?

Answer 28

• T test computes a stat that reflect differences in the means of a variable for two different groups or at two different times for one group eg those who quit smoking and those who didn’t, men and women etc…in these examples one variable differentiates the groups. The variable can be anything that can be measured as a continuous number eg cost per pt visit, age

Question 29

what does covary mean and when do you use it

Answer 29

• When two variables are connected in some way, they are said to be covary-2 variables covary when change in one are connected to consistent changes in the other eg height and wt covary in kids. Another covariance between # times doing procedure anddec of # of errors

Question 30

correlation stat aka

Answer 30

correlation coefficient

Question 31

2 important things about correlation statistic

Answer 31

o Is the number negative or pos. if both inc itll be pos (ht and wt example) in second example above the two variables move in opposite directions=negative relationship o Note the magnitude of the number for a correlation coefficient. Because of how its calculated it can only have a range of values from-1 to +1. A perfect relationship between two variables-as one goes up the other goes up or down in same amount will have a value of either -1 or +1

Question 32

is there generally perfect correlation in real life does covariance tell us the cause of the connection

Answer 32

no | no

Question 33

can you use correlations to predict what kind of stats are these egs of correlational stats (prob not imp)

Answer 33

• correlations are inferential stats that explain about relationships but cant be used to predict because they don’t tell us anything about which variable causes the other variable to change * the most common type of correlation statistic is the Pearson product-moment correlation which uses r to represent that value of the bivariate relationship * other examples of correlation stats: Spearman rho, Kendall tau, and the phi (the stat gives the strength of the covariance between wo variables

Question 34

what kind of test would you use to look for differences among 3 or more groups

Answer 34

analysis of variance tests for differences in the means for three or more groups

Question 35

what does ANOVA tell you and what is the letter that it uses and how is it used

Answer 35

* ANOVA compares how much members of a goup differe or vary from the members of other group * The test stat in ANOVA is usually eh F ratio. Like other stat tests, the final test stat is then compared w a sset of stats one would get if there were no diff between groups * The larger the F ratio the more variation between groups