# The Basic Terminologies of Statistics You Should Know

Statistics is one of the known branches of mathematics subject that is used to study the analytical data. The methods of statistics are generated to examine the large quantitative data and their properties. Different statistics formulas are used by several companies to calculate the report of the individuals or employees. In the upcoming paragraphs, we will discuss terminologies of statistics that is 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. A sample is 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 for the study of statistics for biological, a variety of research fields and topics, public health, or for medical applications. 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, and organization, and presentation of the large data. 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 to interpret several outcomes from the given data and estimate 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.

### 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.

### Quantitative data

• Continuous: It has numeric 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.

### 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:

### What are study analysis that is used as statistics terminology

Statistics analysis is basic statistics terminology that is used to collecting, managing, analyzing, summarizing, manipulating, interpreting, and representing the quantitative data. It can hold all aspects of collected data that involve the techniques for planning of data gathering that is 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:

### Bias

There are three different types of errors that can be generated in different areas od an experiment, such as measurement technique, study design, and analyses. These errors 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 spilling each ting on the floor.

### Descriptive statistics

Descriptive statistics are used for 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.

Outliers: It is known as the extreme of the data points.

### Inferential statistics

Once the data is explored, one needs to recognize which technique should be used to judge the data that aids in detail and 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, which is based on the sample values:

• Confidence Intervals: It is the combination of the standard error and sample statistics to predict the 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 underlying distribution of the given population.
• It is used for the comparisons which are between the two or more paired or independent groups.
• Distribution of the given population 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.

### Observational study

Observation of the existing condition and analysis inferences.

• Case-control: It is used to study the existing set of group dissimilarity on the result, such as w/o vs patients with the disease.
• Cross-sectional: It is the study of the experimental patients one the point at the time.
• Cohort: it is used to study the of the instructions of the group of the same people who are differed 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 that is given to a set of the group which 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: It is the probability of the tests which are shown the difference between the comparisons, supposing the null function as true.

### Sample size justifications

It is the technique terminologies of statistics which is 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.
• It 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.

### 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 that are used in statistics, different kinds of study designs, and the study analysis that are used as statistics terminologies. Because of these terminologies, you can easily understand where and when to use these terminologies.

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