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| any difference between the attitudes of two groups due to some systematic inference and not due to change |
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| the risk associated with not being 100% confident that what you observe in an experiment is due to the treatment or what was being tested |
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CAN BE TRUE OR FALSE
YOU CAN ONLY CHOOSE TO EITHER ACCEPT OR REJECT THE NULL HYPOTHESIS |
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| also known as an error of the first kind, is the wrong decision that is made when a test rejects a true null hypothesis (H0). A type I error may be compared with a so called false positive in other test situations. |
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| also known as a error of the second kind, is the wrong decision that is made when a test fails to reject a false null hypothesis. A type II error may be compared with a so called false negative in other test situations. |
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you are trying to reach conclusions that extend beyond the immediate data alone.
Whenever you wish to compare the average performance between two groups you should consider the t-test for differences between groups. |
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| In statistics statistical inference |
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| is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation. |
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| Most of the major inferential statistics come from a general family of statistical models known as the |
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| examples of General Linear Model |
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| This includes the t-test, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), regression analysis, and many of the multivariate methods |
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In statistics, a result is called statistically significant if it is unlikely to have occurred by chance.
As used in statistics, significant does not mean important or meaningful, as it does in everyday speech. |
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| The amount of evidence required to accept that an event is unlikely to have arisen by chance is known as the |
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| Tests of significance should always be accompanied by |
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| effect-size statistics, which approximate the size and thus the practical importance of the difference. |
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| A statistical hypothesis test is a method of |
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| making decisions using data, whether from a controlled experiment or an observational study (not controlled). In statistics a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. |
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| 3 Commonly Used Statistical Tests |
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| Correlation, T-Test, Chi-Square |
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| association between proportions |
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T Test
TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS |
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| The t-test, one-way Analysis of Variance (ANOVA) and a form of regression analysis are mathematically equivalent and would yield identical results. |
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| T test TESTS BETWEEN THE MEANS OF RELATED GROUPS |
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| The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the analysis for the posttest-only two-group randomized experimental design. |
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