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Inferential statistics powerpoint

This document provides an introduction to inferential statistics, including key terms like test statistic, critical value, degrees of freedom, p-value, and significance. It explains that inferential statistics allow inferences to be made about populations based on samples through probability and significance testing. Different levels of measurement are discussed, including nominal, ordinal, and interval data. Common inferential tests like the Mann-Whitney U, Chi-squared, and Wilcoxon T tests are mentioned. The process of conducting inferential tests is outlined, from collecting and analyzing data to comparing test statistics to critical values to determine significance. Type 1 and Type 2 errors in significance testing are also defined.

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Inferential Statistics Objective: An introduction to what you need to know about statistics
Key Terms Test statistic Critical value Degrees of freedom P value/level Significance Chance Type 1 error Type 2 error Interval Ordinal Nominal
Inferential Statistics Tests Make inferences about the populations from which the samples are drawn
Descriptive Statistics vs. Inferential Statistics Allows us to draw Allow us to say whether conclusions difference is significant Through use of graphs This difference Is 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 We can state how certain we are the results are not Real due to chance difference
P-levels/Significance Levels P ≤0.10 C H P ≤0.05 A N P ≤0.01 C E P ≤0.001 We can also write these as 10%, 5%, 1%, 0.1%
Significant? If our test is significant we can Reject our null hypothesis and accept our alternative/experimental hypothesis If our test is not significant we can Accept 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 of factors: • The type of data • Type of research design (RM vs. IG) • One tailed or two tailed test
Tests to Know Mann Whitney U Chi Squared Wilcoxon T Spearmans rho
Process data Complicated arithmetic Produce test statistic
Sig levels ½’d for one tailed test Compare test Statistic with critical values for 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
Type 1 and Type 2 Errors Type 1 error Rejecting a null hypothesis when we should not P level too tight Type 2 error Accepting a null hypothesis when we should not P level too loose