inferential statistics

Question 1

What is the purpose of inferential statistics

Answer 1

allows us to study samples and then make generalizations about the population.
draws a conclusions about a population by examining the random sample.
Helps researchers test hypotheses and answer research questions, and derive meaning from the results.
a result found to be statistically significant by testing the sample is assumed to also hold for the population from which the sample was drawn.
Enables researchers to:
Estimate population proportions
Estimate population mean
Estimate sampling error
Estimate confidence intervals
Test for statistical significance

Question 2

What are the two main types of methods in inferential statistics

Answer 2

Two main methods:
estimation
the sample statistic is used to estimate a population parameter.
a confidence interval about the estimate is constructed.
hypothesis testing
a null hypothesis is put forward.
Analysis of the data is then used to determine whether to reject it.

Question 3

Differences between null and alternative hypothesis

Answer 3

the alternative (research) hypothesis (H1) is true and the null hypothesis (H0) is not.
in testing differences, the H1 would predict that differences would be found, while the H0 would predict no differences.

Question 4

Tell me about the significance interval and what the most level is

Answer 4

Researchers set the significance level for each statistical test they conduct.
by using probability theory as a basis for their tests, researchers can assess how likely it is that the difference they find is real and not due to chance.
The level of significance is the predetermined level at which a null hypothesis is not supported. The most common level is p < .05.
P =probability
< = less than (> = more than)
If the .05 level is achieved (p is equal to or less than .05), then a researcher rejects the H0 and accepts the H1.

If the .05 significance level is not achieved (p is more than.05), then the H0 is retained.

Question 5

Describe the difference between a type 1 and type 2 error.

Answer 5

Type I error: Erroneously rejecting the null hypothesis. Your result is significant (p < .05), so you reject the null hypothesis, but the null hypothesis is actually true.

Type II error: Erroneously accepting the null hypothesis. Your result is not significant (p > .05), so you don’t reject the null hypothesis, but null hypothesis is actually false.

Question 6

How can you control a type two 2 error

Answer 6

Type II errors can also be controlled by the researcher.
The Type II error rate is sometimes called beta, as a complement to alpha.
How can the beta rate be controlled? The easiest way to control Type II errors is by increase the statistical power of a test.

Question 7

What happens with a larger sample size does the statistical power go up or down

Answer 7

Power is strongly influenced by sample size. With a larger N, we are more likely to reject the null hypothesis if it is truly false.
(As N increases, the standard error shrinks. Sampling error becomes less problematic, and true differences are easier to detect.).
The probability that the statistical test will correctly reject a false null hypothesis
a researcher would like to have a high level of power

Question 8

Factors that affect the power of an experiment

(4) factors

Answer 8

Alpha level
Sample size
Effect size
One-tailed or two-tailed test

Question 9

Describe the power level in more detail here

Answer 9

This is an indication of the size of the treatment effect, its meaningfulness.
With a large effect size, it will be easy to detect differences and statistical power will be high.
But, if the treatment effect is small, it will be difficult to detect differences and power will be low.

Question 10

Give me what the most used CI is

Answer 10

Estimate the population mean or proportion based on the sample survey.
Confidence level: social science standard is 95%.
95% certain that our population estimate is correct within a specified range
This is the precision of the estimates.

Question 11

Hypothesis testing procedure give me the steps

Answer 11

State the hypothesis (H0)
Select the probability level (alpha)
Determine the value needed for significance
Calculate the test statistic
Accept or reject  H0

Question 12

What if you accept the H0 what do you say

Step 1

Answer 12

Ho: no difference between 2 means; any difference found is due to sampling error.
any significant difference found is not a TRUE difference, but CHANCE due to sampling error.

Question 13

Level of signifance reject the HO setting your alpha level

Answer 13

Probability that sample means are different enough to reject Ho (.05 or .01).
level of probability or level of confidence

Question 14

compute the calculated value step 3

Answer 14

Computing Calculated Value

Use statistical test to derive some calculated value (e.g., t value or F value).

Question 15

Step 4 Obtain critical value

Answer 15

Obtain Critical Value
a criterion used based on df and alpha level (.05 or .01) is compared to the calculated value to determine if findings are significant and therefore reject Ho.

Question 16

Reject or accept the null

Answer 16

CALCULATED value is compared to the CRITICAL value to determine if the difference is significant enough to reject Ho at the predetermined level of significance.
If CRITICAL value > CALCULATED value –> fail to reject Ho
If CRITICAL value < CALCULATED value –> reject Ho
If reject Ho, only supports H1; it does not prove H1

Question 17

Parametric vs. Non parametric data

Answer 17

Parametric statistical tests generally require interval or ratio level data and assume that the scores were drawn from a normally distributed population or that both sets of scores were drawn from populations with the same variance or spread of scores.
Nonparametric tests do not make assumptions about the shape of the population distribution. These are typically less powerful and often need large samples.

Question 18

Give me some details about parametric data

What two type of Quantitative data does it go off
is it randomized
are scores evenly distributed
what size does the sample size have to be

Answer 18

Parametric tests are based on a variety of assumptions, such as:
Interval or ratio level scores
Random sampling of participants
Scores are normally distributed 
N = 30 considered minimum by some
Homogeneity of variance
Groups are independent of each other

Question 19

non parametric data - what types of research uses this data

Does it have as much statistical power as others

Answer 19

Considered assumption free statistics.
Appropriate for nominal and ordinal data or in situations where very small sample sizes (n < 10) would probably not yield a normal distribution of scores.
Less statistical power than parametric statistics.

Question 20

Improving the Probability of Meeting Assumptions

Answer 20

Utilize a sample that is truly representative of the population of interest.
Utilize large sample sizes.
Utilize comparison groups that have about the same number of participants.

Question 21

Chi square test
What type of Data is this used on
Think opinions on surveys and who circled a certain anwser x amount of times. For example if you gave a survey on who likes chocolate, vanilla, or strawberry ice cream the calculated the responses on each survey what test would you use?

Answer 21

A nonparametric test is used when the researcher is interested in the number of responses, objects, or people that fall in two or more categories.

Used with crosstabs.
Based on a mathematical formula that looks at the differences between the actual data compared to how the data should have looked if there was no difference.
Used with nominally scaled data which are common with survey research.

Question 22

T-test which kind of data is this good for and what purpose does it sevre?

Answer 22

Test the difference between two sample means for significance.

pretest to posttest
requires interval or ratio level scores
easy to compute
pretty good small sample statistic
relates to research design

Question 23

one group t-test

dependent groups

independent t-test

explain the differences

Answer 23

One-Group t-test
t-test between a sample and population mean
Independent Groups t-test
compares mean scores on two independent samples
Dependent Groups (Correlated) t-test
compares two mean scores from a repeated measures or matched pairs design.
most common situation is for comparison of pretest with posttest scores from the same sample.

Question 24

for Nonparametric data (what type of t-ttest do you use when the data is not randomized or proportionate

Answer 24

Mann-Whitney U-Test This test is used for the independent groups situation.

Wilcoxon Signed-Ranks Test This test is used for the paired samples situation.

Question 25

When is an ANOVA used

Answer 25

Analysis of variance (ANOVA) tests the difference(s) among two or more means.
It can be used to test the difference between two means.
Extension of dependent groups t-test, where each subject is measured on 2 or more occasions.
a.k.a “within subjects design”

Question 26

Pearsons Correlation Rank Coefficient describe when to use it.

Answer 26

Correlation—the extent to which two variables are related across a group of subjects
Pearson r
It can range from -1.00 to 1.00.
0.00 indicates the complete absence of a relationship.
Correlation coefficient: -1.00 is a perfect inverse relationship—the strongest possible inverse relationship.
Correlation coefficient: 1.00 is a perfect positive relationship—the strongest possible direct relationship
The closer a value is to 0.00, the weaker the relationship.
The closer a value is to -1.00 or +1.00, the stronger it is

Question 27

Describe Spearmans rank coefficient

Answer 27

Spearman’s Rank Order Correlation (Greek letter rho)
The Spearman’s rank-order correlation is the nonparametric version of the Pearson product-moment correlation.
Spearman’s coefficient, like any correlation calculation, is appropriate for both continuous, discrete and ordinal variables.

Question 28

Describe differences between inter rater realiablity and intra realiability

I

Answer 28

Reliability is used to describe the overall consistency of a measure. A measure is said to have a high reliability if it produces similar results under consistent conditions. For example, measurements of people’s height and weight are often extremely reliable.
Inter-rater reliability assesses the degree of agreement between two or more raters in their appraisals.
Test-retest reliability assesses the degree to which test scores are consistent from one test administration to the next.
Intra-rater realiability- is when only one person is being studied over a certain period of time doing the same test over and over again and seeing if he or she gets similair results