Critical Analysis in Research Design

Critical analysis is the process of evaluating statements or claims based on evidence.

In Psychology and Neuroscience, critical analysis typically focuses on the strength of evidence supporting a claim.

The "Gambler's fallacy" is the mistaken belief that after a series of similar outcomes (e.g., heads in coin flips), the next outcome is more likely to be different.

Independent events are occurrences that are not affected by previous events. For example, each coin flip is an independent event.

The probability of an event is the likelihood of that event occurring. For example, the probability of getting heads on a fair coin flip is 0.5 or 50%.

Research designs are the methods used to collect and analyze data in research studies.

Correlational studies examine the relationship between two or more variables but don't establish cause and effect.

Experiments involve manipulating an independent variable to observe its effect on a dependent variable, allowing for causal inferences.

Causal inference refers to the ability to determine whether one event caused another event. It's a key goal of experimental research.

Academic skills of critical evaluation are essential for academic success and transferable to various careers and everyday life.

The idea that random events are less common in real-world scenarios compared to purely random sequences.

A test format where students choose one correct answer from multiple options.

The probability of correctly guessing a single-correct answer MCQ, equal to 1 divided by the number of options.

A method in MCQ exams to adjust scores, accounting for the possibility of correct answers due to chance.

A statistical hypothesis test that aims to determine whether there is enough evidence to reject a null hypothesis.

The size of a sample used in a study.

The strength or magnitude of an effect or relationship observed in a study.

The practice of using probability as statistical evidence to support or reject hypotheses.

A statistical hypothesis that assumes there is no difference or relationship between groups being compared.

The significance of an observed effect or relationship in the real world, beyond just statistical significance.

The ability of a study to detect a true effect or difference.

A finding that is unlikely to have occurred by chance alone, based on statistical analysis.

A result that, while statistically significant, is too small to be meaningful in the real world.

A measure of effect size used to quantify the difference between two group means.

A measure of effect size used to quantify the proportion of variance explained by a factor in an analysis of variance.

A study with a small number of participants, making it less likely to detect real effects.

A study with a large number of participants, increasing the likelihood of detecting even small effects.

A finding that is not statistically significant, which could be due to insufficient statistical power or a true lack of effect.

A study where the difference between groups is statistically significant but has little practical relevance.

A research design that aims to establish a causal relationship between variables by manipulating one variable (the independent variable) and observing its effect on another variable (the dependent variable). This involves randomly assigning participants to groups, controlling for extraneous variables, and measuring the outcomes.

A type of research design where researchers observe and analyze naturally occurring variables in a real-world setting without manipulating any variables. This is useful for exploring existing relationships but cannot definitively establish causality.

Controlled experiments are designed to minimize the influence of extraneous variables, while observational studies often have many uncontrolled variables that can influence the observed relationship.

A type of experiment where participants are randomly assigned to groups and are unaware of their assigned condition. This helps to eliminate bias in the results.

The process of drawing a sample from a population in a way that accurately reflects the characteristics of the population. This ensures that the findings of a study can be generalized to the larger population.

A type of observational study that seeks to understand the causes of a certain condition by comparing groups with and without the condition. However, without manipulating any variables, it can only provide indirect evidence of causality.

A research design where researchers study changes in a single group over time, without a control group. This can provide evidence of change but does not definitively establish causality.

In research, correlation refers to a statistical relationship between two variables, meaning that they tend to vary together. However, correlation does not necessarily imply causation. Just because two variables are related doesn't mean one causes the other.