A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s aims, but is controlled because it could influence the outcomes.
Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomization or statistical control (e.g., to account for participant characteristics like age in statistical tests).
Examples of control variables
Does soil quality affect plant growth?
Amount of light
Amount of water
Does caffeine improve memory recall?
Noise in the environment
Type of memory test
Do people with a fear of spiders perceive spider images faster than other people?
A mediating variable (or mediator) explains the process through which two variables are related, while a moderating variable (or moderator) affects the strength and direction of that relationship.
Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. These variables are important to consider when studying complex correlational or causal relationships between variables.
Statistical power, or sensitivity, is the likelihood of a significance test detecting an effect when there actually is one.
A true effect is a real, non-zero relationship between variables in a population. An effect is usually indicated by a real difference between groups or a correlation between variables.
High power in a study indicates a large chance of a test detecting a true effect. Low power means that your test only has a small chance of detecting a true effect or that the results are likely to be distorted by random and systematic error.
Power is mainly influenced by sample size, effect size, and significance level. A power analysis can be used to determine the necessary sample size for a study.
The methods section of an APA style paper is where you report in detail how you performed your study. Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods.
In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample, measures, and procedures used.
In statistics, a Type I error is a false positive conclusion, while a Type II error is a false negative conclusion.
Making a statistical decision always involves uncertainties, so the risks of making these errors are unavoidable in hypothesis testing.
The probability of making a Type I error is the significance level, or alpha (α), while the probability of making a Type II error is beta (β). These risks can be minimized through careful planning in your study design.
If a result is statistically significant, that means it’s unlikely to be explained solely by chance or random factors. In other words, a statistically significant result has a very low chance of occurring if there were no true effect in a research study.
The p value, or probability value, tells you the statistical significance of a finding. In most studies, a p value of 0.05 or less is considered statistically significant, but this threshold can also be set higher or lower.
The standard error of the mean, or simply standard error, indicates how different the population mean is likely to be from a sample mean. It tells you how much the sample mean would vary if you were to repeat a study using new samples from within a single population.
The standard error of the mean (SE or SEM) is the most commonly reported type of standard error. But you can also find the standard error for other statistics, like medians or proportions. The standard error is a common measure of sampling error—the difference between a population parameter and a sample statistic.
A parameter is a number describing a whole population (e.g., population mean), while a statistic is a number describing a sample (e.g., sample mean).
The goal of quantitative research is to understand characteristics of populations by finding parameters. In practice, it’s often too difficult, time-consuming or unfeasible to collect data from every member of a population. Instead, data is collected from samples.
With inferential statistics, we can use sample statistics to make educated guesses about population parameters.