StatisticsStatistics
Hypothesis TestingHypothesis TestingHypothesis is a ‘testable statement’Types = alternate, research,
experimental (H1), null (H0)They are 1 or 2 tailed (directional or non
directional (but not the Null)They include an IV and a DVThey are operationalised (precisely
defined in terms of how the IV and DV will be manipulated or measured)
Aim of research is to accept/reject H1 or H0
Complete the exercise
Inferential StatisticsInferential Statistics1. Analyse using descriptive statistics (tells
you whether a difference exists or not)2. Analyse using inferential statistics (tells you
whether the differences are significant)3. If the sample has yielded significant results,
we can infer the same is true of the population
4. The statistical tests stringently process the data to tell you whether or not chance has caused the outcome
5. Therefore part of the whole process involves having a null hypothesis (HO) and levels of significance or probability e.g. 5%
‘‘Proof’Proof’In science it is only possible to
prove something is not the casee.g. ”all swans are white”Can’t be provedBut can be disproved – HOW?
Significance levelsSignificance levels
Likelihood or probabilityExpressed as a % and as a
decimale.g. heads or tails 50% or 0.5Picking the ace of hearts from 4
aces 25% or 0.25Likelihood of having schizophrenia
1% or 0.01
Ruling out ChanceRuling out Chance
• Standard level of significance in Psychology =
5% (0.05)• Accept H1 – then p < 0.05• Accept H0 – then p > 0.05• BUT a significant result might still
be wrong 5 times in 100 – in other words it happened due to chance – we live with this risk
Type 1 and type 2 errorsType 1 and type 2 errors
Accept H0 Reject H0H0 is actually true
OK Type 1You conclude there is an effect when there isn’t
H0 is actually false
Type 2You conclude there isn’t an effect when there is
OK
Type 1 and type 2Type 1 and type 2Type 1 is more likely when we
have a high significance level e.g. 10%
Type 2 is more likely when we have a low significance level e.g. 1%
At 5% both are equally likely
What you need to be able What you need to be able to doto doIdentify an appropriate statistical
testExplain your choiceState a conclusion based on a
stats testWrite a null hypothesisExplain why a particular stats
test was used
Descriptive StatisticsDescriptive StatisticsDescriptive statistics give us a
way to summarise and describe our data but do not allow us to make a conclusion related to our hypothesis. For example, measure of central tendency such as ____________, ___________ or ____________. It also includes graphs and charts, and measures of dispersion such as _________________ or ______________ _____________.
TasksTasksMeasure of central tendency
◦Match the definitions to the terms, and the strengths and weaknesses.
Measure of dispersion◦What is a range?◦Read about standard deviation
What are bar charts and scatter graphs? Define and draw an example.
Complete the memory experiment tasks
What is the point?What is the point?Why do we bother to use
inferential statistical tests?◦Inferential statistics allow us to draw
conclusions from findings.◦They allow us to see whether our
results are the result of something happening, or are just down to chance.
Cross out the words on the sheet and fill in the table
Pg 20Pg 20Read the example about the chip
bins and female drivers.Read “Using Statistical Tests” on
pg 22-23
Answer the questions on the sheet
Levels of significanceLevels of significanceThis refers to the minimum probability
we will accept that our results are due to chance. If it is too lenient, then our results may appear to be significant when in fact they are not. If it is too stringent, then our results may appear to be insignificant when they actually they are.
In psychology, we generally aim for a significance level of _______%. This means that we can be _________% certain that our results are not due to chance.
This is written as P≤_______
ExampleExampleRead the example about biscuits
On the sheet, cross out the right words and fill in the table
Levels of measurementLevels of measurementNominalOrdinalIntervalRatio
NOIRRead the descriptions on the
sheet give your own examples
Choosing the right testChoosing the right testDesign Nominal data Ordinal data Interval data
Repeated measures
Sign test Wilcoxen signed ranks
Related t test*
Matched pairs
Sign test Wilcoxen Related t test*
Independent measures
Chi square Mann Whitney U
Unrelated t test*
Correlation Chi square Spearmans rho
Pearsons product moment*
* Refers to Parametric tests (see handout)