Karteikarten
Chapter 8
Karten in diesem Stapel (21)
- 1Hypothesis Testingstandardized method scientists use to test whether the data they collect can be interpreted as providing support for their research hypothesis
- 2Hypothesis TestingUsed to determine if the data is measuring the true relationship or whether it happened by chance.
- 3Inferential Statisticsapply statistical sample data to larger population (generalize)
- 4Hypothesis-Testing Flow Chart1. Develop research hypothesis
2. Set alpha (usually a =.05)
3. Calculate power to determine the sample size that is needed
4. Collect data
5. Calculate statistic and p-value
6. Compare p-value to alpha (.05)
p<.05-reject null hypothesis
p>.05-Fail to reject null hypothesis - 5Null hypothesisSymbolized as H0
Assumes observed data do not differ from what would be expected on the basis of chance - 6Research Hypothesis (Example)There is a difference in mean perceived skills scores between the intervention and control groups
- 7Null Hypothesis (Example)There is no difference in mean perceived skills scores between the intervention and control groups.
- 8The Null HypothesisWe hope to reject the null hypothesis
So we can conclude that the observed data were caused by something other than chance alone - 9Testing for Statistical SignificanceBefore we reject the null hypothesis, the observed data must deviate substantially from what would be expected by chance.
- 10Significance levelThe standard the observed data must meet
Also known as alpha (a)
Normally set to a = .05 - 11Comparing the p-Value to Alpha (Probability value)Shows the likelihood of an observed statistic occurring on the basis of the chance
Usually called a p-value
Indicated with the letter p - 12p-ValueIndicates whether the result occurred by chance
Each statistic has an associated p-value
Shows how extreme the data is
We simply compare to alpha
Once alpha level is set and power calculated, a statistic is computed - 13Statistically significantWhen the p-value is less than alpha (p < .05), we reject the null hypothesis
- 14Statistically nonsignificantWhen the p-value is greater than alpha (p > 05), we fail to reject the null hypothesis
- 15a=0.5This means that we may reject the null hypothesis only if the observed data are so unusual that they would occur by chance at most 5% of the time
The smaller the alpha, the more stringent the standard - 16Type 1 errorsOccur when we reject the null hypothesis when it is in fact true
- 17Type 2 errorsOccur when we fail to reject the null hypothesis when the null hypothesis is really false
The probability of making a Type 2 error is called beta, or b - 18Type 2 ErrorFails to reject the null hypothesis when null hypothesis is false
Scientist misses a true relationship
Occur due to random error in measurement
Occur because relationships can be very small
Common when the power of the statistical test is low - 19Determining the Probability of Making ErrorsScientist controls probability of making a Type I error by precisely setting alpha at a small value
Scientist determines probability of making a Type 2 error (beta) by estimating statistical power. Increase statistical power by increasing the sample size. - 20Influence of Sample SizePower of the statistical test is influenced by the actual size of the relationship and the sample size (N) used in the research.
As N increases, the likelihood of a statistically significant relationship between variables and power of the test increases. - 21Basic Rule of ThumbRun as many people in a given research project as is conveniently possible, because when there is more participants, there is also a greater likelihood of detecting the relationship of interest and thus of detecting relationships between variables.