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The contact hypothesis has proven to be highly effective in alleviating prejudice directed toward homosexuals. [24] Applying the contact hypothesis to heterosexuals and homosexuals, Herek (1987) found that college students who had pleasant interactions with a homosexual tend to generalize from that experience and accept homosexuals as a group. [25]
The Contact Hypothesis has been supported by decades of research. Thomas Pettigrew and Linda Tropp’s meta-analysis [4] of over 700 independent samples confirms the contact hypothesis for a variety of minority groups and conservatively estimates the average correlation between contact and prejudice as -.215 (N > 250,000, p < .0001).
[3] That is the meaning intended by statisticians when they say causation is not certain. Indeed, p implies q has the technical meaning of the material conditional: if p then q symbolized as p → q. That is, "if circumstance p is true, then q follows." In that sense, it is always correct to say "Correlation does not imply causation."
Interaction effect of education and ideology on concern about sea level rise. In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive).
The consistent application by statisticians of Neyman and Pearson's convention of representing "the hypothesis to be tested" (or "the hypothesis to be nullified") with the expression H 0 has led to circumstances where many understand the term "the null hypothesis" as meaning "the nil hypothesis" – a statement that the results in question have ...
Graphical model: Whereas a mediator is a factor in the causal chain (top), a confounder is a spurious factor incorrectly implying causation (bottom). In statistics, a spurious relationship or spurious correlation [1] [2] is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third ...
If two variables are uncorrelated, there is no linear relationship between them. Uncorrelated random variables have a Pearson correlation coefficient, when it exists, of zero, except in the trivial case when either variable has zero variance (is a constant). In this case the correlation is undefined.
In scientific research, the null hypothesis (often denoted H 0) [1] is the claim that the effect being studied does not exist. [note 1] The null hypothesis can also be described as the hypothesis in which no relationship exists between two sets of data or variables being analyzed. If the null hypothesis is true, any experimentally observed ...