Term
| Distinguish between descriptive statistics & inferential statistics |
|
Definition
Descriptive stats- statistical measures that describe the results of a study; descriptive stats include measures of central tendency, variability, and correlation
inferential stats- statistics designed to determine whether results based on sample data are generalized to a population. |
|
|
Term
| define and give example: NULL HYPOTHESIS |
|
Definition
The hypothesis that the variables under investigation are not related in the population, that nay observed effect based on sample results in due to random error.
i.e. in a model, no-model study of childrens behaviors the H1 (null hypothesis) is that the population mean of teh no-model group is equal to the population mean of the model group.
(H2 [research hypothesis]: the population mean of the no-model group is not equal to the population mean of the model group) |
|
|
Term
| Define and give example: RESEARCH HYPOTHESIS |
|
Definition
The hypothesis that the variables under investigation are related in the population-that the observed effect based on sample data is true in the population.
i.e. in a model, no-model study of childrens behavior H2 (the research hypothesis) is that the population mean of the no-model group is not equal to the population mean of the model group.
(H1 [null hypothesis]: the population mean of the no-model group is equal to the population mean of the model group) |
|
|
Term
| Compare/contrast Null Hypothesis & Research Hypothesis |
|
Definition
Null Hypothesis: the population means are equal-the observed difference is due to random error. IV had no effect.
Research Hypothesis: The population means are not equal. IV did have an effect. |
|
|
Term
| Define: Statistical Significance |
|
Definition
Significance indicates that there is a low probability that the difference between the obtained sammple means was between the obtained sample means was due to random error. significance, then, is a matter of probability.
[a significant results is one that has a very low probability of occuring if the population means are equal. |
|
|
Term
| Rejecting the null hypothesis |
|
Definition
If we can determine that the null hypothesis is incorrect, then we accept the research hypothesis as correct. Acceptance of the research hypothesis means that the IV had an effect on the DV.
[pg. 246] |
|
|
Term
How is probability used in statistics?
and what does p < .05 mean? |
|
Definition
Probability in statistical inference is used to specify the probability that an event will occur if there is no difference in the population.
p < .05 means Probability of 5% or less that the obtained data are consistent with the null hypothesis |
|
|
Term
| Why is sample size important? |
|
Definition
As the size of your sample increases, you are more confident that your outcome is actually different from the null hypothesis expectation.
The more observations sampled, the more likely you are to obtain an accurate estimate of the true populaton value. |
|
|
Term
| What is a test statistic? |
|
Definition
Different statistical tests allow us to use probability to decide whether to reject the null hypothesis.
t-test & F-test |
|
|
Term
|
Definition
The t test is most commonly used to examine whther two groups are significantly different from each other.
when asking whether the mean of the no-model group differs from the mean of the model group. |
|
|
Term
|
Definition
| The F test is a ore general statistical test that can be used to ask whether there is a difference among three of mroe groups tor to evaluate the results of factorial designs. |
|
|
Term
| To use a statistical test, you must first specify ________________________ |
|
Definition
| The null hypothesis and the research hypothesis that you are evaluating. And teh significance level that you will use to decide whether to reject the null hypothesis ( this is the alpha level) researchers generally use a significance level of .05 |
|
|
Term
|
Definition
The probability required for significance, usually .05
(pg. 248) |
|
|
Term
| The F statistic is a ratio of two types of variance. what are they? |
|
Definition
Systematic variance: the deviation of the group means from the grand mean, or the mean score of all individuals in all groups. Systematic cariance is small when the difference between groups means is small and increases as the group mean differences increase.
-> the variability of scores between groups
Error Variance: the deviation of the individual scores in each group from their respective group means.
-> the variability of scores within groups |
|
|
Term
|
Definition
| The extent to which two variables are associated. In experimental research, the magnitude of the impact of the independent variable on the dependent variable. |
|
|
Term
|
Definition
Sq Root of (t^2)/(t^2 + df)
df: degrees of freedom
N1 + N2 - 2
or total number of participants in the groups minus the number of groups. |
|
|
Term
|
Definition
|
|
Term
|
Definition
group difference / within-group variability
group difference: difference between the means of the two groups
Within Group Variance: Variance of each group by the number of subjects in the group and add them together then take the Sq Rt of the result. |
|
|
Term
|
Definition
| When we reject the null hypothesis but the null hypothesis is actually true. |
|
|
Term
|
Definition
| When the null hypothesis is accepted although in the population the research hypothesis is true. |
|
|
