Loading in 5 sec....

1.State your research hypothesis in the form of a relation between two variables.PowerPoint Presentation

1.State your research hypothesis in the form of a relation between two variables.

Download Presentation

1.State your research hypothesis in the form of a relation between two variables.

Loading in 2 Seconds...

- 107 Views
- Uploaded on
- Presentation posted in: General

1.State your research hypothesis in the form of a relation between two variables.

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

1.State your research hypothesis in the form of a relation between two variables.

2. Find a statistic to summarize your sample data and convert the above into statistical hypothesis:

Statistical hypothesis:r > 0m1 - m2 < 0

3. Set a straw man, i.e., null hypothesis

Null hypothesis:r = 0m1 - m2 = 0.

4.Set the alpha level and conduct the statistical test with the assumption that the null is true.

5. Make a decision with potential errors.

Sampling Distribution of a Statistic

Imagined and theoretical

μ=72

μ=72

PopulationSampling Distribution

Sample size N = 36

μ=72

μ=72

μ=72

Sample Size N = 16

μ=72

μ=72

Sample Size N = 36

μ=72

μ=72

The mean of the sampling distribution of means (any statistic) equals the population mean (any parameter).

The standard deviation of the sampling distribution of means (any statistic) equals the population standard deviation divided by the square root of sample size. This is called the standard error of means.

The sampling distribution of means is normal independent of the pattern of the population distribution, given a large enough sample size (e.g., n = 30)

An example:

Hypothesis: Chinese children today are overweight.

Choose a statistic: Mean weight

Past records: m = 50 lb; s = 30 lb

H1: m > 50 lb

H0: m = 50 lb

a<.01

n = 225 children ages 7 to 9;

Reject Null

μ=50

2.32

Point estimate:

Interval estimates:

CI90

1.64

-1.64

An example:

Hypothesis: Children’s weight differs from past.

Choose a statistic: Mean weight

Past records: m = 50 lb; s = 30 lb

H1: m 50 lb

H0: m = 50 lb

a<.01; two tails, a<.01/2 or a<.005 at each tail

n = 225 children ages 7 to 9;

-2.58

μ=50

2.58

Null Hypothesis

Actually True

Actually False

NOT reject

Decision

Reject

H0: μ = 50

Reject Null

.05

z = 1.96

μ= 50

H1: μ > 50

power

β

H0: μ = 50

Reject Null

.01

z = 1.96

μ= 50

H1: μ > 50

power

β

Large N

H0: μ = 50

Reject Null

.05

μ= 50

z = 1.96

H1: μ > 50

power

β

Small N

H0: μ = 50

Reject Null

.05

μ= 50

z = 1.96

H1: μ > 50

power

β