1. Research methods

2. 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.

3. 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.

4. Today
Introduction to inferential statistics Null hypothesis Level of significance Probability Type 1 and type 2 errors After Christmas Carrying out statistical tests Picking the right test Writing up the report

5. Introduction to inferential statistics Why not just use descriptive stats?
Descriptive statistics give us convenient and easily understood summaries of the data but we can’t draw any firm conclusions from them, they are just an overview.
In order to draw firmer conclusions and to accept or reject hypotheses, inferential statistics are needed.

6. Inferential statistics
Inferential statistics are used to test hypothesis.
Do groups differ on some outcome variable?
Is the difference more than expected by chance
Used to make generalisations from a sample to a population.
Inferential statistics take into account sampling error (chance, random error)

7. Null hypothesis: What is a 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

8. 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 it is rejected then we must retain the alternative hypothesis and vica versa.
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

9. 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.

10. A teacher in a small secondary school wanted to find out whether there was any truth in her idea that students who used a computer regularly for their homework achieved higher exam grades than those who did not. She decided to interview a sample of 30 students taken from across the school. She tape-recorded all the interviews. She later obtained their end of year exam grades from their reports.
What is the IV?
whether the participants used a computer regularly for their homework or didn’t use a computer regularly for their homework.
What is the DV?
Exam grade achieved
Null hypothesis:
There will be no difference between the exam grades achieved at the end of year between those who regularly used a computer to complete homework and those who did not regularly use a computer to complete homework; any differences are due to chance factors.

11. Level of significance What is statistical significance then?!
Inferential statistics is a test of significance because it is designed to assess whether we reject or retain the null hypothesis. If the null hypothesis is retained, the result is not significant; if it is rejected the result is significant. Inferential statistical tests work by assessing the probability of our results occurring due to chance alone (rather than the IV) We use it to determine if the probability of our results being down to chance is low enough for our alternative hypothesis to be accepted.

12. P value
The reason for calculating an inferential statistic is to get a p value (p = probability)
Inferential statistical tests work by assessing the probability of our results occurring due to chance alone (rather than the IV)
The p value determines whether or not we reject the null hypothesis.
We use it to estimate whether or not we think the null hypothesis is true. The p value provides an estimate of how often we would get the obtained result by chance, if in fact the null hypothesis were true.
If the p value is small, reject the null hypothesis and accept that the samples are truly different with regard to the outcome.
If the p value is large, accept the null hypothesis and conclude that the treatment or the predictor variable had no effect on the outcome.

13. Probability and Significance
Probability, or p, is expressed as a number between 0 and 1.
0 means an event will not happen.
1 means that an event will definitely happen.
The P value will always be found to be between 0 and 1 due to the way in which it is calculated.

14. Activity
To give you an idea and to keep things easy complete the following exercise using 0 – 100 rather than 0 - 1   
On a scale of rate the following statements for probability 0 = Impossible and 100 = Certain (Remember the only thing certain is death and the only thing impossible immortality!)
You winning the lottery
It raining in the next week
Dreaming of elephants this week
Having a day off ill this term
Becoming a famous entertainer
Becoming a parent in your lifetime
A cure for cancer being discovered in your lifetime
If you spin a coin it will come down heads
If you spin two coins they will both come down heads
Passing all you’re A levels

15. Because we used a scale between your answers are expressed as percentages. Convert them into fractions and then decimals e.g. 50% is ½ or 0.5 (move the decimal point two places to the left. This converts the probability to a decimal between 0 and 1)

16. Decision rules – Levels of significance
How small is "small?“
Once we get the p value (probability) for an inferential statistic, we need to make a decision. Do we accept or reject the null hypothesis? What p value should we use as a cutoff?
The one chosen is called the level of significance.

17. The aim of inferential statistics is to discover if your results are statistically significant.
A statistically significant result is one which is unlikely to have occurred through chance.
Levels of significance
Researchers can use significance levels of 10%, 5%, 1% (or 0.1% in very stringent conditions) - expressed as:
10%, 0.10, 1 in 10, p≤0.10.
5%, 0.05, 1 in 20, p≤0.05
1%, 0.01, 1 in 100, p≤0.01
If you use a 5% statistical significance level and this is achieved you are saying that the probability of your results being a fluke and nothing to do with your IV is less than 5%.
or you are 95% sure that your change in DV is because of your IV

18. Using the 0.05 level of significance means if the null hypothesis is true, we would get our result 5 times out of 100 (or 1 out of 20). We take the risk that our study is not one of those 5 out of 100. When you use a computer program to calculate an inferential statistic (such as a t-test, Chi-square, correlation), the results will show an exact p value (e.g., p = .013).

19. 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.
Null Hypothesis: - Any difference between the two conditions is due to chance.

20. Type 1 and type 2 error Type 1 and Type 2 errors
By using significance levels, we are always at risk of either rejecting the null hypothesis when it is true or retaining the null hypothesis when it is false. This is why scientific evidence can never be taken as fact, and theories are never proved.
A type 1 error occurs when a null hypothesis is rejected when it should not have been. In other words, they have accepted their results as significant when, in fact, they are down to chance alone. The likelihood of a type 1 error mirrors the level of significance employed. For example, at p = the risk of making a type 1 error is 5% (1 in 20). A type 1 error is sometimes known as a false positive.
A type 2 error occurs when a null hypothesis is retained when it should not have been. In other words, we have accepted that our results are down to chance, when in fact they are not. A type 2 error is often referred to as a false negative. It is more likely to occur with a higher level of significance (e.g. p=0.01).

21. Recap:
Outline the difference between descriptive statistics and inferential statistics?
The null hypothesis predicts that there will be a significant difference? True/false.
Shorthand for the null hypothesis is Ho? True/false
What are Inferential statistics?
Why are Levels of measurement important?
Ordinal data is data that is measured on a scale? True/false
Why is it necessary to have a Null hypothesis?

22. Outline the difference between descriptive statistics and inferential statistics?
Summarising data vs. allowing you to see whether the research hypothesis or null hypothesis is retained
The null hypothesis predicts that there will be a significant difference? True/false.
False
Shorthand for the null hypothesis is Ho? True/false
True
What are Inferential statistics?
Tests designed to assess whether we reject or retain the null hypothesis.
Why are Levels of measurement important?
To know which is the most appropriate descriptive statistic to calculate, which graph to use and which inferential test to use we need to establish what the level of measurement is.
Ordinal data is data that is measured on a scale? True/false
Why is it necessary to have a Null?
Eliminates bias. Forces researcher to accept the view that the two sets of data has occurred through chance. Means there is no other conclusions that can be made

23. Key Terms for Statistical Analysis
Probability
Psychologists look at data to see if the pattern of results could have occurred by chance. If there results did not occur by chance then we say they are significant.
Significance
You need to have a null hypothesis (H0) and an alternative hypothesis (H1). What we are looking for is a significant (large) difference in results so that the differences seen in our samples are different and not due to chance; we want to accept the alternative hypothesis.
Chance
Normally psychologists set the probability level a p≤0.05 which means there is a 5% possibility the results occur by chance in the sample, when there was no real difference in the results in the general population.
Observed value
The rho or u value calculated is called the observed value.
Critical value
You need to look in a table of critical values to see if the results are significant. You need to know the 1) degrees of freedom (df) – normally the number of ppts in a study (N); 2) one- or two-tailed test; 3) significance level – normally p≤0.05