inferential statistics

probability

the likelihood that something will happen. psychologists are interested to see from their data analysis how likely it was that their results were due to chance.

psychologists work out how likely it is that chance has affected their results. probability (p) is expressed as a value between 0 and 1.

probability and significance

Inferential statistics allow psychologists to make conclusions based on the probability that a particular pattern of results could have arisen by chance. but, if it could not have arisen by chance or if it’s extremely unlikely to have arisen by chance, then the pattern is described as significant.

Judging whether an effect is significant or not can’t be done just by looking at averages or other forms of descriptive analysis. Instead, inferential statistical tests must be carried out to ascertain whether results are significant (i.e. whether they are likely to have been down to chance or not).

‘chance’ and ‘significance level’

psychologists can’t be absolutely certain that an observed effect was definitely not down to chance no matter how strong the effect seems to be. but, they can state how certain they are.

In general, psychologists use a probability p ≤ 0.05, meaning there’s less than or equal to a 5% probability the results did occur by chance.

whenever a psychologist is carrying out research which leads to inferential testing, the psychologist has to make a decision how much of their findings they want to attribute to chance.

In some studies, psychologists want to be more certain such as when they are conducting a replication of previous research or considering the effects of a new drug on health (cuz here in particular we would want to be very careful about taking chances). In these situations, researchers use a more stringent probability such as p ≤ 0.01, (here the psychologist would be 99% sure the IV had caused an effect in the DV but would attribute 1% that the change in the DV was down to another chance factor). In other situations, a more lenient level such as p ≤ 0.10 might be used, such as when conducting research into a new topic. This chosen value of 'p' is called the significance level.

type 1 error

when the researcher believes there’s an effect but in reality there isn't one, aka false positive or, the error of optimists. wrongly accept the alternate hypothesis and reject null hypothesis, when it’s actually true. This is the worst type of error cuz false conclusions could do lots of damage e.g. incorrect diagnoses.

A Type 1 error may occur when the probability level is too lenient, e.g. 10%.

type 2 error

a psychologist may reject the experimental hypothesis and accept the null hypothesis when they should be accepting the experimental hypothesis and rejecting the null hypothesis. often referred to as a false negative.

may occur when the probability level is too strict, e.g. 1%

more likely to occur when using a non-directional hypothesis cuz a non-directional hypothesis states that the results could go in either direction.