There are different types of parametric tests to determine if a statistically significant difference is present.
An independent-samples t-test is used when comparing two groups on one continuous dependent variable that is normally distributed.
A paired-samples t-test is used when you look at the differences of one group across two time periods or observation periods.
ANOVA tests are used when we are examining more than two groups or more than two observation periods.
Slide 3: Our null hypothesis states that no difference occurs.
If we overlapped the distribution curves of the two groups or the two observation periods, we would find that they were similar in shape.
Slide 4: If the differences in the mean scores between the groups or observation …show more content…
Smaller sample sizes result in larger standard deviations with a flatter and more spread out distribution.
This results in greater estimates of standard error. This is a problem.
Slide 7: Let’s talk about beer. Yes, beer.
W.S. Gossett, a scientist for Guiness brewery, pondered how each bale of barley affected the quality of the beer produced.
As a result of his work, he produced the Probably Error of the Mean and the t-test Table which consider the differences between two groups by looking at their means while weighing their sample error that is influenced by sample size.
Slide 8: Another term that you will report about your statistical results or findings is the ‘degrees of freedom.’
This term refers to how many values are free to vary in a set of values if we know the summary statistic and the number of values in the set; how many values would we need to know in order to deduce the remaining values.
It is sued to compensate for the bias that occurs when calculating for large standard deviations found in small sample sizes.
The formula is N or the total number in your sample if you use one sample minus …show more content…
Slide 13:
If we looked at the row titled “Level of Significance of 1-Tailed Test’, a significance level of 0.0005 is found.
Again, I want to emphasize that SPSS will calculate whether or not a significant change has occurred.
We can say that our obtained t statistic of 4.34 is greater than 3.373 or the probability of obtaining a t statistic of 4.34 is less than .001 (p <.001). This p value is below our .05 alpha level. The probability of obtaining the difference of between the variables, if the null hypothesis were true, is extremely low.
Slide 14:
An independent Samples t-test is appropriate to use if we are determining if a statistically significant difference in the dependent variable occurs between two groups, using a post-test only two group design.
Slide 15:
Let’s talk about the independent samples t-test, the test that is used to compare the means between two groups.
As with any test, we always have assumptions and we try not to violate these
An independent-samples t-test is used when comparing two groups on one continuous dependent variable that is normally distributed.
A paired-samples t-test is used when you look at the differences of one group across two time periods or observation periods.
ANOVA tests are used when we are examining more than two groups or more than two observation periods.
Slide 3: Our null hypothesis states that no difference occurs.
If we overlapped the distribution curves of the two groups or the two observation periods, we would find that they were similar in shape.
Slide 4: If the differences in the mean scores between the groups or observation …show more content…
Smaller sample sizes result in larger standard deviations with a flatter and more spread out distribution.
This results in greater estimates of standard error. This is a problem.
Slide 7: Let’s talk about beer. Yes, beer.
W.S. Gossett, a scientist for Guiness brewery, pondered how each bale of barley affected the quality of the beer produced.
As a result of his work, he produced the Probably Error of the Mean and the t-test Table which consider the differences between two groups by looking at their means while weighing their sample error that is influenced by sample size.
Slide 8: Another term that you will report about your statistical results or findings is the ‘degrees of freedom.’
This term refers to how many values are free to vary in a set of values if we know the summary statistic and the number of values in the set; how many values would we need to know in order to deduce the remaining values.
It is sued to compensate for the bias that occurs when calculating for large standard deviations found in small sample sizes.
The formula is N or the total number in your sample if you use one sample minus …show more content…
Slide 13:
If we looked at the row titled “Level of Significance of 1-Tailed Test’, a significance level of 0.0005 is found.
Again, I want to emphasize that SPSS will calculate whether or not a significant change has occurred.
We can say that our obtained t statistic of 4.34 is greater than 3.373 or the probability of obtaining a t statistic of 4.34 is less than .001 (p <.001). This p value is below our .05 alpha level. The probability of obtaining the difference of between the variables, if the null hypothesis were true, is extremely low.
Slide 14:
An independent Samples t-test is appropriate to use if we are determining if a statistically significant difference in the dependent variable occurs between two groups, using a post-test only two group design.
Slide 15:
Let’s talk about the independent samples t-test, the test that is used to compare the means between two groups.
As with any test, we always have assumptions and we try not to violate these