Chapter 5: Hypothesis Testing and Statistical Significance

Non Parametric

Non Parametric

________ or Distribution- Free Tests: where statistical tests do not make assumptions about the underlying distributions or estimate the particular population parameters.

Alpha

________ simply gives an indication of the likelihood of finding such a relationship if the null hypothesis were true.

Parametric tests

________ assume that we are dealing with normally distributed data.

Distribution

________- free or non- parametric tests are based upon the rankings or frequency of occurrence of your data rather than the actual data themselves.

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.

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.

Replication

________ is one of the cornerstones of science.

Statistical significance

________ does not equal psychological significance.

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.

Alpha

________ is the probability that we will get a relationship of an obtained magnitude if the null hypothesis were true.

Parametric tests

________ are more powerful because they use more of the information from your data.

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.

Parametric tests

________ are used very often in psychological research because they are more powerful tests.

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

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

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

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

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

Not Significant

our findings when we find that our pattern of data is highly probable if the null hypothesis were true

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

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

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

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

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

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

Non-Parametric or Distribution-Free Tests

where statistical tests do not make assumptions about the underlying distributions or estimate the particular population parameters

You might ask

what do you mean by approximately equal