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## PowerPoint Slideshow about 'Hypothesis Testing and Results Interpretation' - oya

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Types of Hypothesis

- 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.

Alternative/Research Hypothesis

- 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?

Hypothesis testing

- Core of Inferential Statistics
- contributes to the science of education primarily by expanding, refining, or revising its knowledge base.

Hypothesis Testing Procedure

- 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.

P from Fancy Schmancy Software

- 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

Examples & Exercise

- 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

Examples & Exercise

- 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

Examples & Exercise

- 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

Shaprio-W Normality Test

- Seems to have a different interpretation
- But only used in Analyse-it
- The higher the p, the more normal the distribution is.

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