Probability and Significance Flashcards
What do all statistical tests end with and what is it crucial for determining?
A number - the calculated value
Crucial in determining whether the researcher has found a result that is statistically significant and consequently, whether they should accept the alternative or the null hypothesis
How do researchers begin their investigations?
By writing a hypothesis (directional or non-directional depending how confident the researcher is in the outcome)
If it is an area with no previous research then the researcher will write a null-hypothesis (no significant difference) rather than an alternate/research
What establishes significance?
Statistical tests
What is significance?
A statistical term that tells us how sure we are that a difference or correlation exists. A significant result means that the researcher can reject the null hypothesis
What will all statistical tests employ?
A significance level - the point at which the researcher can claim to have discovered a significant difference or correlation
For psychologists what is the usual level of significance and what does it mean?
0.05, usually written as p≤ 0.05
This means that the probability that the observed effect occurred by chance is less than or equal to 5%
Once a statistical test has been calculated, what must be done to check for statistical significance?
The calculated value must be compared with a critical value - a number that tells us whether or not we can reject the null hypothesis and accept the alternative hypothesis
What does each statistical test have and how does this differ?
Own table of critical values developed by statisticians
For some statistical tests, the calculated value must be equal to or greater than the critical value- this is true with any statistical test with ‘R’ in its name
For other tests the calculated value must be equal to or less than the critical value
How does the researcher know which critical value to use?
One-tailed test is used if the hypothesis was directional whereas a two tailed test is used it if was non-directional
Number of participants appears as the N value on the table. For some tests, degrees of freedom (df) are calculate instead
The level of significance - the 0.05 levels is the standard level of psychology
Due to psychologists never being 100% certain that they have found statistical significance what is possible?
(Usually up to 5% due to the accepted P value in psychology), that the wrong hypothesis may be accepted
What is a type I error?
When the null hypothesis is rejected and the alternative hypothesis accepted when it should have been the other way round because in reality the null hypothesis is ‘true’
This is often referred to as an optimistic error or a false positive as the researcher claims to have found a significant difference or correlation when one does not exist
What is a type II error?
The reverse of type I - when the null hypothesis is accepted but it should have been the alternative hypothesis because, in reality, the alternative hypothesis is true. This is pessimistic error or ‘false negative’
When are the two types of errors likely to occur?
More likely to make a type I error if the level of significance is too lenient e.g. 0.1 0r 10% rather than 5%
A type II error is more likely if the significance level is too stringent e.g. 0.01 or 1% as potentially significant values will be missed
This is why psychologists favour the 5% significance value as it balances the risk of Type I and Type II errors
