The research process generally starts with an observation. After the observation, relevant theories are consulted and hypotheses are generated, from which predictions are made. After that, data is collected to test the predictions and finally the data is analysed. The data analysis either supports or does not support the hypothesis. A theory is an explanation or set of

There are three main misconceptions of statistical significance:

1. A significant result means that the effect is important
   Statistical significance is not the same as practical significance.
2. A non-significant result means that the null hypothesis is true
   Rejecting the alternative hypothesis does not mean we accept the null hypothesis.
3. A significant result means

Bias can be detrimental for the parameter estimates (1), standard errors and confidence intervals (2) and the test statistics and p-values (3). Outliers and violations of assumptions are forms of bias.

An outlier is a score very different from the rest of the data. They bias parameter estimates and have an impact on the error associated

Variance of a single variable represents the average amount that the data vary from the mean. The cross-product deviation multiplies the deviation for one variable by the corresponding deviation for the second variable. The average value of the cross-product deviation is the covariance. This is an averaged sum of combined deviation. It uses the following formula:

Researchers should not compare artificially created groups in an experiment (e.g. based on the median). There are several problems with median-splits:

1. Median splits change the original information drastically
2. Effect sizes get smaller
3. There is an increased chance of finding spurious effects

CATEGORICAL PREDICTORS IN THE LINEAR MODEL
Comparing the difference between

The overall fit of a linear model is tested using the F-statistic. The F-statistic is used to test whether groups are significantly different and then specific model parameters (the bs) are used to show which groups are different.

The F-statistic gives an associated p-value as well. A p-value which is smaller than 0.05 (or any set

Factorial designs are used when there are more than one independent variables. There are several factorial designs:

1. Independent factorial design (between groups)
   There are several independent variables measured using different entities.
2. Repeated-measured (related) factorial design
   There are several independent variables using the same entities in all conditions.
3. Mixed design
   There are

Mixed designs are a combination of repeated-measures and independent designs. It includes some independent variables that were measured using different entities and some independent variables that used repeated measures.

The most important assumptions of the mixed designs ANOVA are sphericity and homogeneity of variance.

Factor analysis and principal component analysis (PCA) are techniques for identifying clusters of variables. These techniques have three uses: understanding the structure of a set of variables (1), construct a questionnaire to measure an underlying variable (2) and reduce a dataset to a more manageable size while retaining as much of the original information as possible (3).