Statistics is a subfield of mathematics that deals with qualitative and quantitative data. Various statistics techniques are used to analyze data in order to extract great insights. After the data processing, the outcome or conclusion helps the organizations make critical decisions to avoid losses and grow by leaps and bounds.
But when we study statistics, we get confused with many terms such as sample, population, mean, variance, etc. A person who studies statistics theory for the first time may fail to understand the meaning of these terms, and consequently, that person does not understand what the text is about.
Do you find the same difficulty when you work on statistics projects and assignments?
Do not worry if you face the same problem with the terminologies of statistics. In this blog, we will cover the important terms of statistics and what they mean.
Basic Terminologies of Statistics
In statistics, we use various terms that are specific to this field, such as sample and population, etc. In the further paragraphs, we will enunciate important terms of statistics.
Population: Population refers to the entire group of individuals you want to study, such as persons, things, and objects.
Sample: Sample is a subset of the population that has all the features and characteristics of the population. If a sample does not possess the characteristics of the population, it is useless.
For example, the total number of students in a particular school is considered population, but the numbers(students) in a single class or section is known as a sample.
Variable Types: In statistics, we use two qualitative variable types: Ordinal and Nominal.
In ordinal variable types, we have the words such as always, never, sometimes, often, etc.
Whereas in nominal variables, we find unordered qualitative variables such as skin color, gender, etc.
We use two types of data in statistics: Qualitative and quantitative. Quantitative data is further categorized into two parts: Continuous and Discrete.
Continuous: Values that are not fixed and change over time come under continuous data such as height, weight, temperature etc. These values can be represented through a line graph to show how data changes with time.
Discrete Data: Discrete data is fixed data that don’t have to be whole numbers. For example, the shoe size of a person is 7.5. It can’t be 7.52
(in whole numbers). Discrete data always refers to certain values.
Data visualization: Data visualization is the way to represent the data through tables, graphs, charts, etc.
Statistics Analysis: Statistics is all about the collection, management, analysis, summarization, manipulation, interpretation, and representation of quantitative data.
Statistics analysis is of three types.
Bias- Bias term is related to the errors in the data experiments like measurement techniques, study design, and analysis.
Types of error
Random(indeterminate) error: It can be found by statistical data.
Systematic(determinate) error: Reference standards can identify such errors.
Gross Error: Gross errors are errors that a reader makes while reading the data, such as spellings.
Descriptive Statistics- Descriptive statistics is a useful concept to measure the average or the standard deviation that helps to judge the data in a descriptive statistics manner. This type of statistics is comparatively easy from the inferential statistics.
It gives an idea about the comparison(similarities and differences)of the collected data.
Apart from this, descriptive statistics is used to classify the gathered data by tables, graphs, charts, etc.
Statistics- All the characteristics of a population are known as statistics.
Parameter- All the characteristics of a sample are known as parameters.
Location Measurement: Location measurement determines the place or position of the data.
Mean: Mean refers to the average of all the information and data.
mean = addition of all the numbers/ total terms
Median- Center point of the given data is known as the median.
Mode- The most frequent or repetitive value in the given data.
Frequency: It is the ratio of the given values of one variable from the different variables.
Outliers: Extremes of the data points are referred to as outliers.
Inferential Statistics- Inferential statistics is a type of statistics that deals with the hypothesis of data. It is useful in drawing a conclusion of the population standard that is based on sample values.
Inferential statistics focus on the findings of descriptive statistics and use these findings to draw a conclusion.
In this blog we have discussed the basic terminologies of statistics and their role in the statistics process. I hope now you are familiar with these terms and will not find any difficulty whenever you need to work on statistical concepts.
Statistics is a valuable field to make a career and we use the statistical concepts from our kitchen to our profession. We should know how statistics works and what is the meaning of different terms in statistics to work with transparency.