# The Basic Terminologies of Statistics You Should Know Statistics is one of the known branches of mathematics that is used to study analytical data. The methods of statistics are generated to examine the large quantitative data and their properties.

Several companies use different statistical formulas to calculate the report of the individuals or employees. In the upcoming paragraphs, we will discuss terminologies of statistics that are used to study for different purposes.

To initiate, one needs first to recognize the definition of the statistics in terms of sample data and population data

The samples are one of the sub-set of the population, whereas a population is an overall set of things or individuals of a specified group.

The characteristics of the sample data are known as statistics, and the characteristics of the population data are known as parameters.

Biostatistics is used to study statistics for biological, a variety of research fields and topics, public health, or medical applications.

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The main objective of it is to use the proper statistical techniques to get knowledge about the parameters that can affect the health of humans.

### What Is Statistics?

Statistics is the study of the analysis, presentation, collection, interpretation, organization, and large data presentation. It can be defined as a function of the given data.

That is why statistics are combined with classifying, presenting, collecting, and arranging the numerical information in some meaningful manner.

It also facilitates interpreting several outcomes from the given data and estimating all possibilities for the upcoming applications.

With the help of statistics, one can find several measures of central data as well as the deviations of dissimilar values from the main values.

Before proceeding to the advanced terminologies of statistics, let’s check whether you understand basic terminologies of statistics, i.e., population and sample.

Select the population and sample from the given statements:

## What Are The Variable Types That Are Used For Terminologies Of Statistics?

Here are some of the variable types that are used for terminologies of statistics:

### 1. Categorical (qualitative)

• Ordinal: It has ordered qualitative variables in the given data such as sometimes, always, never, frequently, and much more.
• Nominal: It has unordered qualitative variables in the collected data such as gender, hair color, and much more.

### 2. Quantitative data

• Continuous: It has numerical variables with an infinite number of collected values, such as height and much more.
• Discrete: It consists of numeric variables that are easily counted, such as the number of bacteria and others.

### 3. Visualizing data

• Tables: It has numeric conclusions of the percent values, frequencies, summary statistics, and much more.
• Graphs: It is used to represent the different numeric data in the form of:
• Scatterplot: It is used to plot the two numeric variables.
• Histogram: It can represent the data in a bar graph view of frequencies.
• Boxplot: It can present the median, mean, range, and quartiles of the collected data. Example:

Determine the primary terms that are refereed in the following studies

• Sample
• Population
• Data
• Variable
• Statistics
• Parameter

Solution:

• Sample
• Population
• Data
• Variable
• Statistics
• Parameter

Solution:

#### 3. A study was designed for testing the automobiles’ safety; the NTSB gathered and analyzed the data related to the effects of the automobiles crashed over the tested dummies. Below is the criterion, which they have used:

The dummies placed at the front seat of the car were crushed on the wall at the speed of 35miles/hour. Now, we have to understand the proportion of dummies that had head injuries. We have taken the samples of almost 75 cars.

• Sample
• Population
• Data
• Variable
• Statistics
• Parameter

Solution:

### What Is Study Design That Is Used As Terminologies Of Statistics?

Statistics analysis is basic statistical terminology used to collect, manage, analyze, summarize, manipulate, interpret, and represent quantitative data

It can hold all aspects of collected data that involve the techniques for planning data gathering based on the structure of experiments and surveys. Statistics analysis is used to calculate the data and represent it in various trends.

Statistics analysis are based on three different types:

## 1. Bias

There are three different types of errors that can be generated in different areas of an experiment, such as measurement technique, study design, and analyses.

The margin of error can either be under or overestimate the parameters and to false summaries. These three types of error are:

• Random (indeterminate) error: It can evaluate the statistics data.
• Systematic (determinate) error: It can evaluate with the help of reference standards.
• Gross error: It can use to big mistakes, such as spelling each thing on the floor.

## 2. Descriptive statistics

Descriptive statistics are used for the measurement of the average or the standard deviation that aids in judging the data in a descriptive statistics manner.

It can be taken as the most interesting technique to obtain the different data in columns or levels of the parameters. Descriptive statistics provides an idea of the differences or similarities between the gathered data.

It can be used to characterize the collected data using tables, graphs, and numerical conclusions.

• Frequency: It is the proportion of the given data values, which is of a single variable from various variables.
• Outliers: It is known as the extreme of the data points.

## 3. Inferential statistics

Once the data is explored, one needs to recognize which technique should be used to judge the data that help in detail, visualize the analysis, and make the necessary summaries about the collected data.

There are several statistical techniques that are used to deal with various kinds of experimental data and evaluate the required relationship between the given data.

It can be used to draw a summary of the population standard, which is based on the sample values:

• Confidence Intervals: It is the combination of the standard error and sample statistics to predict the larger population parameters.
• Standard Error: It is the uncertainty of the sample average.
• Statistical Tests: These tests are used to quantify the connection between comparisons.
• A statistical test can be performed depending on the number of comparisons, variable type, and the given population’s underlying distribution.
• It is used for the comparisons which are between the two or more paired or independent groups.
• The given population’s distribution can be non-parametric (no supposed distribution) or parametric (normally distributed).
• Types of statistical tests: z-test, chi-square, regression, t-test, f-test, ANOVA, correction, and much more.

### Study Type

• Observational study

Observation of the existing condition and analysis inferences.

• Case-control: It is used to study the existing set of group dissimilarities on the result, such as w/o vs patients with the disease.
• Cross-sectional: It is the study of the experimental patients one point at a time.
• Cohort: it is used to study the instruction or step of the group of the same people who are different on certain parameters to check the effect of these factors on the result of the interest.
• Experimental

The analysts randomly assign the task to the people for treating the groups.

• Randomization: These are the methods that are used for selecting the samples of the specific constant variables across the standardization (groups) so that the real effect can be examined.
• Placebo: It is the treatment given to a set of the group that does not have therapeutic effects.
• Blinding: It is the assignment for the treatment which is unknown for the doctor, patients, or both.
• Hypothesis

It is the detailed prediction of the scientific questions which are tested:

• Null hypothesis: In the null hypothesis, there is no relation between the set of groups.
• Alternative hypothesis: In this, there is a relation between the set of groups.
• P-value: The probability of the tests showed the difference between the comparisons, supposing the null function as true.

### Sample sizes justifications

The technical terminologies of statistics are used to make sure that there must be enough experiment to search a statistics difference between the set of the group when they are biologically different.

• Significance level (α): It has the threshold where the null hypothesis can be rejected. Standard values of α consist of 0.05, 0.01, 0.001.
• If the value of p is greater than α, then the test fails in the category of the rejected null hypothesis.
• If the value of p is equal to or less than α, then the null hypothesis can be rejected.
• Effect size: It is used to check the difference between the comparison values.
• Power: It is the ability to detect the difference between the truly existed values.

Let’s take a quick revision of all the terminologies of statistics we have discussed above!!

### Conclusion

This blog has provided all the information about the basic terminologies of statistics that is used to study the large qualitative data.

This includes the types of variables used in statistics, different kinds of study designs, and the study analysis used as statistics terminologies.

Because of these terminologies, you can easily understand where and when to use these terminologies.

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### Q1. What are the different definitions of statistics?

1. Statistics is a branch of mathematics that deals with the data collection, examination, description, and display of numerical data masses.
2. Statistics is a collection of various quantitative data.

### Q2. Who is the father of statistics?

Father of statistics: Professor Prasanta Chandra Mahalanobis is the father of Indian Statistics.

### Q3. What are the 6 types of statistics?

Here are the 6 types of statistics that you must know which are as follows:

1. Descriptive
2. Exploratory
3. Inferential
4. Predictive
5. Causal
6. Mechanistic