inferential statistics powerpoint
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Inferential Statistics
Objective:An introduction to what you need to
know about statistics
Key Terms
Test statisticCritical valueDegrees of freedomP value/levelSignificanceChanceType 1 errorType 2 errorInterval OrdinalNominal
Inferential Statistics Tests
Make inferences about the populations from which the samples
are drawn
Descriptive Statistics vs. Inferential Statistics
Allows us to draw conclusions
Through use of graphs
Allow us to say whetherdifference is significant
This differenceIs significant
Probability
Inferential tests use probability to ascertain the likelihood that a pattern of results could have arisen by chance.
If the probability of the results occurring by chance is below a certain level we assume these results to be significant
Chance
Real difference
We can state how certain we are the results are not
due to chance
P-levels/Significance Levels
CHANCE
P ≤0.10P ≤0.05P ≤0.01P ≤0.001
We can also write these as 10%, 5%, 1%, 0.1%
Significant?
If our test is significant we canReject our null hypothesis and accept our alternative/experimental hypothesis
If our test is not significant we canAccept our null hypothesis and reject our alternative/experimental hyp
Levels of measurement
Nominal
Ordinal
Interval
Levels of data: nominal
• Which newspaper paper do you read regularly?
• We can put these into categories.
Levels of Data: ordinal
• What grade did you get for each of your gcse’s?
• These can be put in order… highest to lowest
Levels of data: interval
• How quick is your reaction time?
• We can measure and compare the exact time because the intervals on the ruler are equal.
Inferential Tests
Which test to use depends upon a number offactors:• The type of data• Type of research design (RM vs. IG)• One tailed or two tailed test
Tests to Know
Mann Whitney UChi SquaredWilcoxon T
Spearmans rho
Process
dataComplicated arithmetic
Produce test statistic
Compare testStatistic
with critical valuesfor that test
To determine significance level
critical value: Value that test statistic must reach in order for null hyp to be rejected
Sig levels ½’d for one tailed test
Sig levels ½’d for one tailed test
Type 1 and Type 2 Errors
Rejecting a null hypothesis when we should notP level too tight
Accepting a null hypothesis when we should notP level too loose
Type 1 error
Type 2 error