The Ultimate Inferential Statistics Quiz

In psychology, the general rule is that researchers will allow for a 5% (0.05) probability of their results being due to chance. 5% balances out the possibility of making what are called Type 1 and Type 2 errors.

The null hypothesis is typically denoted as H0 in statistical tests, while alternative hypotheses may be denoted as H1 or something else, depending on the context.

The statement p≤0.05 refers to the probability of results occurring by chance. In statistical analysis, p-value is used to determine the significance of the results. A p-value of 0.05 indicates that there is a 5% chance that the observed results are due to random chance. If the p-value is less than or equal to 0.05, it is considered statistically significant, suggesting that the results are unlikely to have occurred by chance alone. Therefore, the correct answer is that p≤0.05 means the probability of results occurring by chance is equal to or less than 0.05.

A p-value represents the probability of obtaining the observed results if there is no real effect in the population. Smaller p-values suggest stronger evidence against the null hypothesis.

The purpose of an inferential statistical test is to check the probability of your results applying to the wider population. This is done by analyzing a sample of data and making inferences or predictions about the larger population from which the sample was drawn. It helps to determine if the findings from the sample can be generalized to the entire population, providing a level of confidence in the results.

The correct answer is the null hypothesis because it states that there will be no difference between the length of time taken to complete tests online and tests completed on paper, and if there is any difference, it is due to chance. The null hypothesis is often used in statistical hypothesis testing to assume that there is no relationship or difference between variables, and any observed difference is simply due to random variation. In this case, it is being hypothesized that the mode of test completion (online or on paper) does not have a significant impact on the time taken to complete the test.

Making a Type 1 error is more likely if a researcher has set their level of significance too high (e.g. 10%). In other words, they have allowed a 10% probability that their results are due to chance...a bit risky!

You are comparing data from a group of males with data from a group of females.

The Chi-Squared test is used where the data is nominal, the design is independent groups and you are looking to see if there is a difference between the two groups.

You just have to learn these I'm afraid!

The data is nominal as you have two discrete categories of results; male and female.

An independent t-test is used to compare means of two independent groups or samples.

The Mann Whitney test is a non-parametric test used to compare two independent groups. In this case, the observed value being less than the critical value suggests that there is a significant difference between the two groups being compared. Therefore, the student should reject the null hypothesis, which states that there is no difference between the groups, and accept the experimental hypothesis, which suggests that there is a significant difference. This indicates that the student's data supports the idea that there is a meaningful relationship or effect between the variables being studied.

If, after collecting all your data, your findings didn't seem to support your original hypothesis, there would be no point trying to generalise it out to the wider population!

If you can put individual participant responses in order (85, 92,97 etc.) the data is ordinal; if you can put individual participant responses into discrete categories (Cornflakes, Rice Krispies etc.) the data is nominal.