Chapter 5: Hypothesis Testing and Statistical Significance

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27 Terms

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Non Parametric
________ or Distribution- Free Tests: where statistical tests do not make assumptions about the underlying distributions or estimate the particular population parameters.
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Alpha
________ simply gives an indication of the likelihood of finding such a relationship if the null hypothesis were true.
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Parametric tests
________ assume that we are dealing with normally distributed data.
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Distribution
________- free or non- parametric tests are based upon the rankings or frequency of occurrence of your data rather than the actual data themselves.
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null hypothesis
Type I Error: where you decide to reject the ________ when it is in fact true in the underlying population; you conclude that there is an effect in the population when no such effect really exists.
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P value
________: the probability of obtaining the pattern of results we found in our study if there was no relationships between the variables in which we were interested in the population.
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Replication
________ is one of the cornerstones of science.
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Statistical significance
________ does not equal psychological significance.
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test statistic
The ________ (e.g., correlation coefficient or t- value) remains the same for both one- and two- tailed tests on the same set of data.
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Alpha
________ is the probability that we will get a relationship of an obtained magnitude if the null hypothesis were true.
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Parametric tests
________ are more powerful because they use more of the information from your data.
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Alpha
________ (α): the criterion for statistical significance that we set for our analyses; it is the probability level that we use as a cut- off below which we are happy to assume that our pattern of results is so unlikely as to render our research hypothesis as more plausible than the null hypothesis.
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Parametric tests
________ are used very often in psychological research because they are more powerful tests.
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p-value
the probability of obtaining the pattern of results we found in our study if there was no relationships between the variables in which we were interested in the population
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Null Hypothesis
always states that there is no effect in the underlying population; by effect we mean a relationship between two or more variables, a difference between two or more different populations or a difference in the responses of one population under two or more different conditions
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Research Hypothesis
our prediction of how two variables might be related to each other; alternatively, it might our prediction of how specified groups of participants might be different from each other or how one group of participants might be different when performing under two or more conditions
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Alpha (α)
the criterion for statistical significance that we set for our analyses; it is the probability level that we use as a cut-off below which we are happy to assume that our pattern of results is so unlikely as to render our research hypothesis as more plausible than the null hypothesis
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Statistically Significant
our findings when we find that our pattern of research results is so unlikely as to suggest that our research hypothesis is more plausible than the null hypothesis
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Not Significant
our findings when we find that our pattern of data is highly probable if the null hypothesis were true
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Type I Error
where you decide to reject the null hypothesis when it is in fact true in the underlying population; you conclude that there is an effect in the population when no such effect really exists
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Type II Error
where you conclude that there is no effect in the population when in reality there is an effect in the population; it represents the case when you do not reject the null hypothesis when in fact you should do because in the underlying population the null hypothesis is not true
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One-tailed Hypothesis
on where you have specified the direction of the relationship between variables or the difference between 2 conditions; also called a directional hypothesis
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Two-tailed Hypothesis
one where you have predicted that there will be a relationship between variables or a difference between conditions, but you have no predicted the direction of the relationship between the variables or the difference between the conditions; also called a bi-directional hypothesis
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The test statistic (e.g., correlation coefficient or t-value) remains the same for both one
and two-tailed tests on the same set of data
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If we make a one-tailed prediction, we would predict which of the above scenarios is most appropriate
that is, which condition will have the higher scores
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Non-Parametric or Distribution-Free Tests
where statistical tests do not make assumptions about the underlying distributions or estimate the particular population parameters
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You might ask
what do you mean by approximately equal