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# Hypothesis Testing and Results Interpretation - PowerPoint PPT Presentation

Hypothesis Testing and Results Interpretation. By Minjuan Wang ED 690 Educational Technology. Types of Hypothesis. Null hypothesis There is no change, difference or relationship between A and B A starting point or a benchmark

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### Hypothesis Testing and Results Interpretation

By Minjuan Wang

ED 690

Educational Technology

• Null hypothesis

• There is no change, difference or relationship between A and B

• A starting point or a benchmark

• Many articles have implied null hypothesis, but may not clearly stated.

• Few studies are designed to verify the nonexistence of a relationship

• Coffee maker is broken

• no relationship between its broken and the humming birds on the tree outside my balcony.

• Hypothesis that is implicitly accepted if the null hypothesis is rejected

• Directional (one-tailed test)

• Non-directional (two-tailed test)

• Hypothesis: not to be proven but to be supported

• Does your study fail if your hypothesis is not supported by the data?

• Core of Inferential Statistics

• contributes to the science of education primarily by expanding, refining, or revising its knowledge base.

• Come up with a hypothesis

• Set a: level of risk you are willing to take; or the cut-off point for a test result to be significant

• Select the test

• Compute the obtained value: t, f, r, etc.

• Find the critical value in the respective table

• To reject a null hypothesis->Obtained value must be > critical value

• Otherwise, fail to reject null hypothesis (H0)

• But, never “accept” null hypothesis

• Many tests are needed to confirm that A is not different from or associated with B.

• When rejecting null-P, the alternative 2-tailed P is implicitly accepted.

• Inferential statistics

• T, ANOVA, Correlation, Regression

• P is the probability of chance (indicator of significance)

• Free us from the test tables

• Results vary

• P<.05

• P<.001

• P=.013 (the exact probability of the outcome/effect due to chance—SPSS)

• Outcome: difference, change, or association

• P>.05 or p=ns (nonsignificant)

• The probability of rejecting a null-P exceeds 0.05 (the cut-off point) (Salkind)

• So reject it

• Scenario:

• two groups of patients: anti-depression drug group; and placebo group

• Run t for two (null-HP)

• t(58)=2.45, p<.05

• t: the test that was used

• 58: degree of freedom

• 2.45: the obtained value

• P: the probability of chance is within the cut-off point (level of significance/or risks allowed)

• Significant mood difference exists between the two groups

• So the treatment (drug) did work

• Scenario:

• two groups of patients: anti-depression drug group; and placebo group

• Run t for two

• t(58)=0.14, p>.05

• t: the test that was used

• 58: degree of freedom

• 0.14: the obtained value

• The probability of rejecting the null-p exceeds the cut-off point, so reject it!

• Also means, the probability of chance exceeds the cut-off point (level of significance/or risks allowed)

• No significant mood difference exists between the two groups

• So the treatment (drug) did not work

• Scenario:

• two groups of patients: anti-depression drug group; and placebo group

• Run t for two

• t(58)=0.14, p=.891

• t: the test that was used

• 58: degree of freedom

• 0.14: the obtained value < critical value 2.001

• Not extreme enough for us to conclude the difference is due to treatment

• P: the exact probability that the outcome (difference) is due to chance

• Also means, the probability of chance exceeds the cut-off point (level of significance/or risks allowed)

• No significant mood difference exists between the two groups

• So the treatment (drug) did not work

• Seems to have a different interpretation

• But only used in Analyse-it

• The higher the p, the more normal the distribution is.