USING DATA TO ANSWER STATISTICAL QUESTIONSThe information we gather with experiments and surveys is collectively called data. Statistics is the art and science of learning from data. Statistical problem solving consists of four things:Formulate a statistical questionCollect dataAnalyse dataInterpret resultsThe three main components of statistics for answering a statistical question are:DesignStating the goal and/or statistical question of interest and planning how to obtain data that will address them. (e.g: how do you conduct an experiment to determine the effects of ‘X’)DescriptionSummarizing and analysing the data that are obtained (e.g: summarizing people’s tv-habits in ‘hours of tv watched per day’)InferenceMaking decisions and predictions based on the data for answering the statistical question. (predicting the outcome of an election, based on the description of the data)Probability is a framework for quantifying how likely various possible outcomes are. SAMPLE VERSUS POPULATIONThe entities that are measured in a study are called the subjects. This usually means people, but it can also be schools, countries or days. The population is the set of all the subjects of interest. In practice, we usually have data for only some of the subjects who belong to that population. These subjects are called a sample. Descriptive statistics refers to methods for summarizing the collected data. The summaries usually consist of graphs and numbers such as averages and percentages. Inferential statistics are used when data are available from a sample only, but we want to make a decision or prediction about the entire population. Inferential statistics refers to methods of making decisions or predictions about a population, based on data obtained from a sample of that population. A parameter is a numerical summary of the population. A statistic is a numerical summary of a sample taken from the population. The true parameter values are...

THE ASSOCIATION BETWEEN TWO CATEGORICAL VARIABLESWhen analysing data the first step is to distinguish between the response variable and the explanatory variable. The response variable is the outcome variable on which comparisons are made. If the explanatory variable is categorical, it defines the groups to be compared with respect to values for the response variable. If the explanatory variable is quantitative, it defines the change in different numerical values to be compared with respect to values for the response variable. The explanatory variable should explain the response variable (e.g: survival status is a response variable and smoking status is the explanatory variable).An association exists between two variables if a particular value for one variable is more likely to occur with certain values of the other variable. A contingency table is a display for two categorical variables. Conditional proportions are proportions which formation is conditional on ‘x’. A conditional proportion should be conditional to something. A conditional proportion is also a percentage. The proportion of the totals (e.g: percentage of total amount of ‘no’) is called a marginal proportion.There is probably an association between two variables if there is a clear explanatory/response relationship, that dictates which way we compute the conditional proportions. Conditional proportions are useful in determining if there’s an association. A variable can be independent from another variable. THE ASSOCIATION BETWEEN TWO QUANTITATIVE VARIABLESWe examine a scatterplot to study association. There is a difference between a positive association and a negative association. If there is a positive association, x goes up as y goes up. If there is a negative association, x goes up as y goes down. Correlation describes the strength of the linear association. Correlation (r) summarizes th direction of the association between two quantitative variables and the strength of...

It is important to both produce and consume research. A research consumer is important, because to effectively know something or to put a theory or treatment in to use, it is imperative that the research consumer knows the evidence behind the evidence-based treatment. It is important to be able to decide how valuable and useful a research really is.Both research producers and research consumers share an interest in psychological phenomena, such as behaviour or emotion. They also both share a commitment to the practice of empiricism: to answer psychological questions with systematic observations. The cupboard theory is the idea that young animals (but also your dog) clings on to the caregiver because the caregiver provides food. The contact comfort theory is the idea that young animals (but also your dog) clings on to the caregiver because the caregiver provides warmth and contact comfort. These theories have been tested and followed the empirical cycle.THE EMPIRICAL CYCLEThe empirical cycle always starts with an observation.Induction -> Theory -> Deduction -> Prediction ->Testing -> Results -> Evaluation -> Observation - > InductionObservationYou make an observation. This can be based on past research or an ‘every day method’. InductionThis is the process of coming up with a theory that explains your observation. In this phase you research your research question. TheoryAfter you’ve researched your research question you can find or come up with a theory. A theory is a set of statements that describe general principles about how variables relate to one another. A good theory is supported by data from previous studies, it should be falsifiable; it has to be possible to debunk the theory and a theory should not be unnecessarily complex. This is called parsimony. (preferring the simplest theory)DeductionThis is the process of formulating a prediction that follows from your theory. You make an hypothesis: a...