# Statistical Terminology That Is Particularly Important

Are you searching for a resource to guide you through the powerful world of statistics? Are you looking for a short introduction to statistics or an extensive list of terminology that can help you better understand statistical language?

We have compiled different statistics and statistical phrases that will help you better communicate with those who work in the field.

Statistics terms:

## Type 1 Error: The False Positive

This error occurs when the researcher concludes that a relationship exists between two variables when in reality it doesn’t. This error is also known as a type I error or significance test.

## Type 2 Error: The False Negative

This is the opposite of a type I error. Instead, it is the error that occurs when the researcher concludes that there is no relationship between two variables when in reality, there actually is. This error is also known as a type II error or a nonsignificance test.

## Recall/Sensitivity

This measures the percentage of things that were correctly detected by the individual or research.

## Precision

It measures the accuracy of the measure. How good is your model in making accurate predictions? The signal to noise ratio is a figure that compares the accuracy of a measure to the volume of noise it encounters.

## Frequency/Predictability

It looks at the percentage of votes that come out in favor of something. For example, this is useful to calculate the likelihood that a group will vote out a certain candidate. We can also utilize it to predict the success of an individual candidate.

## Accuracy

It measures how close the output of a research is to the actual target outcome. It is usually calculated as a percentage by dividing the number of accurate instances by the total number of instances in which research was conducted.

## Specificity

It refers to the percentage of things incorrectly detected as non-compliant by the individual or research.

## True Positive (TP)

A True Positive occurs when an individual has a positive test result and has the condition. If a person is truly positive for a disease, they are referred to as true positives. However, you should note that symptoms may not always be present. For example, a blood test may be very good at detecting the presence of syphilis even when the individual shows no symptoms.

## True Negative (TN)

A True Negative occurs when an individual has a negative test result and does not have the condition. For example, if a person tests negative for HIV, they are referred to as true negatives.

Variable: A characteristic, trait, or attribute that can change. In statistics, the variable that is being measured will determine what kind of statistic will be created to represent and analyze the data. Examples include age group, gender, income level, political affiliation, and intelligence level.

Nominal: A coding system for variables in which each value has its own name.

Experiment: It is a research method used to test a hypothesis by manipulating one or more factors. Examples of factors that can be manipulated include different levels of nutrients in prospective foods, different levels of pain medicines, and different concentrations of solutions. After the manipulation is completed, factors are assessed to determine what effects were produced by the manipulation.

Empirical Data: Data that has been collected via experimentation or observation. It is often collected in the short-term.

Field Saturation: When the whole unit area being studied has been sampled for a statistic (e.g., frequency or percentage).

Fielder’s Distance: A statistical distance measure measuring one variable against another, given a third variable.

Fisher’s Exact Test: This is a statistical test for determining if results from two independent experiments can be considered to have come from the same population.

Fitting a Model: A decision about, or a process of determining, the parameters of a mathematical function, based on the data. The parameters are values that describe how the function varies with respect to the data. In other words, they tell us how much the function changes when we vary certain variables.

Forming an Association: Association is defined as two occurrences of an event occurring in close proximity in time or space.