Starter: Descriptive Statistics Answer the following questions on the hand out

A2: what you need to cover Reliability across all methods of investigation. Ways of assessing reliability: test-retest and interobserver; improving reliability. Types of validity across all methods of investigation: face validity, concurrent validity, ecological validity and temporal validity. Assessment of validity. Improving validity. Content analysis Features of science: objectivity and the empirical method; replicability and falsifiability; theory construction and hypothesis testing; paradigms and paradigm shifts. Reporting psychological investigations. Sections of a scientific report: abstract, introduction, method, results, discussion and referencing.

A2: what you need to cover Inferential testing Students should demonstrate knowledge and understanding of inferential testing and be familiar with the use of inferential tests. Introduction to statistical testing; the sign test. Probability and significance: use of statistical tables and critical values in interpretation of significance; Type I and Type II errors. Factors affecting the choice of statistical test, including level of measurement and experimental design. When to use the following tests: Spearman’s rho, Pearson’s r, Wilcoxon, Mann-Whitney, related t-test, unrelated t-test and Chi-Squared test.

Today Introduction to inferential statistics Null hypothesis Level of significance Probability Type 1 and type 2 errors Still to cover Monday: Carrying out statistical tests / Picking the right test Tuesday: Sign test Thursday: practice questions Monday: 45 minute research method exam.

Why do we need to conduct statistical tests? Statistical tests tell us the significance of a set of findings- did the IV really effect the DV or were the findings a fluke?! The more significant a finding is the more effect the IV had on the DV With an experiment looking at differences between conditions, we need to establish the probability that the results are significant, in other words, that two sets of data are different enough to conclude that the IV has made the changes occur in the DV

Probability, significance and the null hypothesis

Null hypothesis Two hypothesis are formulated at the beginning of a study: The alternative hypothesis (H1) Predicts that there will be a significant difference Directional or non directional (one tailed or two tailed) The null hypothesis (HO) predicts that there will not be a significant difference

What is a null hypothesis and why do we need one? The null hypothesis predicts that any difference between two or more sets of data will have occurred through chance alone If the null hypothesis is rejected we say our results are statistically significant. If it is accepted we say they are NOT significant. We focus on the null hypothesis because it eliminates bias from the research by forcing the researcher to consider the view that any difference found between the two sets of data has occurred through chance alone

A team of psychologists was interested in studying the effects of alcohol on peoples' reaction times. Earlier research suggested that an increase in reaction time was due to the alcohol rather than peoples' expectations of alcohol. The psychologists recruited two groups of volunteers (an independent groups design) from a local university. Each participant's reaction time was measured by using a computer game. The participants were then given a drink. The first group received a drink containing a large measure of strong alcohol; the second group received an identical drink without alcohol, but with a strong alcoholic smell. Finally, all participants were required to play the computer game again to assess their reaction time. Once they had completed the task, they were then thanked for their time and allowed to leave. What is the IV? whether the participants have had an alcoholic drink or one that is not alcoholic but smells as if it is What is the DV? reaction times on a computer game Null hypothesis: There will be no difference between the university students‘ reaction times on a computer game between those who have had an alcoholic drink or one that is not alcoholic but smells as if it contains alcohol; any differences are due to chance factors.

Probability: We need to use inferential statistics to tell us if the result that we have found is due to chance or not To establish if our results are reliable we have to look at the probability of a result being due to chance or not This is known as: level of significance The minimum accepted level of probability commonly used in psychology is 5%, this is represented as 0.05 If the level of significance achieved from a test is equal to or less 0.05 than the results are said to be significant This would mean that we are 95% sure that the IV caused the change in the DV

Probability: Can be expressed as: A proportion: a 1 in 5 chance. As a percentage: 20% More commonly expressed as a decimal in psychology: 0.2. In psychology: 10%=0.10, 5%=0.05, 1%=0.01 and 0.1%=0.001 To go from % to decimal divide by 100, move decimal place 2 spaces to the left. Remember the more stringent (lower) the level of significance you set the more significant the results are

Observed value: Every time you perform a statistical test you get an OBSERVED VALUE This observed value tells you the extent to which your results are valid, you then have to compare this observed value to a table of CRITICAL VALUES to see of your results are significant or not To be significant the observed value should be greater or less than the critical value depending on the type of testNote that there will be a different table of values for different statistical tests

Interpreting results: Usually in psychology if the results are significant it means that the probability of the result being due to chance is 5% or less We express our results in terms of the Null Hypothesis, if a result is statistically significant we can reject the null hypothesis. If the result is not statistically significant we must accept the null hypothesis. P<0.05 means the results are significant- so we would accept the experimental hypothesis and reject the null hypothesis

Interpreting results: P is used to represent “the probability that is due to chance” > =means greater than < =means less than ≥ means greater than or equal to ≤ means less than or equal to SO……………… P<0.05 means that the probability that the result is due to chance is less than 5%

Complete the handout on past exam questions

Type 1 and type 2 errors: The 5% level of significance has been accepted as it represents a reasonable balance between the chances of making a type 1 or type 2 error These can occur because: Level of probability accepted is either too lenient (too high) or too stringent (too low)

Type 1 and type 2 errors Type 1 error: Type 2 error: Occurs when we conclude that there IS a significant difference when there is NOT This can happen if the accepted level of probability is set TOO LENIENT Significance level set at 20% Type 2 error: Occurs when we reject the experimental hypothesis and accept the null when there IS a difference This can happen if the probability level is TOO STRINGENT Significance level set at 1%

Complete the exam questions Type 1 and type 2 error Complete the exam questions

Monday Introduction to statistics How to choose your statistical test