Understanding extraneous variables

In an experiment, an extraneous variable is any variable that you’re not investigating that can potentially affect the outcomes of your research study.

If left uncontrolled, extraneous variables can lead to inaccurate conclusions about the relationship between independent and dependent variables.

Research question Extraneous variables
Is memory capacity related to test performance?
  • Test-taking time of day
  • Test anxiety
  • Level of stress
Does sleep deprivation affect driving ability?
  • Road conditions
  • Years of driving experience
  • Noise
Does light exposure improve learning ability in mice?
  • Type of mouse
  • Genetic background
  • Learning environment

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How to report numbers and statistics in APA style

The APA style guide is commonly used for reporting research results in the social and natural sciences. This article walks you through APA style standards for reporting statistics in academic writing.

Statistical analysis involves gathering and testing quantitative data to make inferences about the world. A statistic is any number that describes a sample: it can be a proportion, a range, or a measurement, and so on.

When reporting statistics, use these formatting rules and suggestions from the APA style guide where relevant.

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What is a within-subjects design?

In experiments, a different independent variable treatment or manipulation is used in each condition to assess whether there is a cause-and-effect relationship with a dependent variable.

In a within-subjects design, or a within-groups design, all participants take part in every condition. It’s the opposite of a between-subjects design, where each participant experiences only one condition.

A within-subjects design is also called a dependent groups or repeated measures design because researchers compare related measures from the same participants between different conditions.

All longitudinal studies use within-subjects designs to assess changes within the same individuals over time.

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What is a between-subjects design?

In experiments, you test the effect of an independent variable by creating conditions where different treatments (e.g. a placebo pill vs a new medication) are applied.

In a between-subjects design, or a between-groups design, every participant experiences only one condition, and you compare group differences between participants in various conditions. It’s the opposite of a within-subjects design, where every participant experiences every condition.

A between-subjects design is also called an independent measures or independent-groups design because researchers compare unrelated measurements taken from separate groups.

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Random assignment in experiments

In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomization.

With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomized designs.

Random assignment is a key part of experimental design. It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors.

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Control variables explained

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
Research question Control variables
Does soil quality affect plant growth?
  • Temperature
  • Amount of light
  • Amount of water
Does caffeine improve memory recall?
  • Participant age
  • Noise in the environment
  • Type of memory test
Do people with a fear of spiders perceive spider images faster than other people?
  • Computer screen brightness
  • Room lighting
  • Visual stimuli sizes

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Mediator vs moderator variables

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.

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Statistical power explained

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.

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How to write an APA methods section

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.

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Type I and Type II errors

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.

Example: Type I vs Type II error
You decide to get tested for COVID-19 based on mild symptoms. There are two errors that could potentially occur:

  • Type I error (false positive): the test result says you have coronavirus, but you actually don’t.
  • Type II error (false negative): the test result says you don’t have coronavirus, but you actually do.

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