Statistics is a field of mathematics. It is a science that primarily concerns the gathering, organization, analysis, interpretation, and presentation of data. When it comes to statistical analysis, there are two key principles to consider. These are descriptive and inferential statistics. Descriptive statistics use charts and tables to examine large amounts of data. It never uses a sample to conclude.
In contrast, researchers use inferential statistics to test hypotheses. Students examine the sample and conclude the population. This blog, written by the experts at AllAssignmentHelp focuses on descriptive and inferential statistics to provide students with a piece of deeper knowledge about the subject, study opportunities, its applications, related work fields, etc.
Definition of Inferential Statistics
To have a better grasp of the phrase inferential statistics, first consider the term population in statistics. When it comes to statistics, the term “population” refers to more than only human beings. Rather, it refers to the complete raw data for the analysis. There are occasions when one must examine data that is incomplete. In this case, data analysis must be done using samples. For example, you may wish to collect statistics on the number of cancer patients under the age of 18 worldwide. There may not be an exact number of patients listed here. Here, you utilize sample data from a given population.
In such instances, inferential statistics provides tools that allow us to make good use of samples to perform inductive reasoning on population data. Here, the researcher can draw a broad conclusion. Researchers utilize the sampling procedure to reflect the population as accurately as feasible. Data science is a field that heavily relies on inferential statistics.
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Approaches Associated with Inferential Statistics
The possibilities of achieving perfect accuracy throughout the sample procedure are minimal. Sometimes sampling causes inaccuracies and differences in interpretation. Therefore, researchers prefer to employ inferential statistics.
There are two common approaches to inferential statistics:
Parameter estimation
These are descriptive estimates of the entire raw population. They are also known as sample statistics in this case, as they work with a random sample of the population. In parameter estimation, the researcher uses a sample to get the population estimate. This estimate is not exact.
Statistical hypothesis testing
This approach of inferential statistics allows us to derive inferences about the entire population based on a sample.
Definition of Descriptive Statistics
Descriptive statistics are short constants used to summarize or brief a data collection. This data collection might be the complete population or a sampling from it.
It is divided into two main categories i.e., measurements of central tendency and measures of dispersion or variability.
Measures of central tendency comprise the median and mode. It is useful in expressing the center point of a frequency distribution for a particular raw data set. Various metrics of central tendency are better suited to different contexts and settings.
- Mean: This has been used with continuous data. It is sometimes referred to as the arithmetic average.
- Median: It divides the data into two equal pieces. One-half of the data is less than that quantity, and the other half is bigger. The median can be used for both continuous and ordinal data.
- Mode: Mode contains a vast amount of data. It is used to represent categorical data.
The following measures are used for describing the various groups in descriptive statistics:
- Central tendency: The data set’s center may be found using the mean, mode, and median. It delivers a descriptive overview of the entire dataset in a single number.
- Dispersion: Descriptive statistics can be extended by assessing dispersion. Dispersion may be measured using the standard deviation and variance ranges.
- Skewness: It is the distortion in a bell curve or a normal distribution. One can determine if the distribution of values is symmetrical or skewed.
Differences Between Descriptive and Inferential Statistics
- The use of descriptive statistics researchers has complete raw population data. On the other hand, most of the researchers take the help of inferential statistics when the raw population data is in large quantities and cannot be compiled or collected.
- The use of descriptive statistics is when sampling is not required. Whereas, the sampling process is required as the analysis is based on sample parameters.
- Properties of the raw population are Mean, median, and mode are known as descriptive statistics parameters. On the flip side, properties of the sampling data in the inferential statistics are not termed as parameters but rather pronounced as statistics.
- This type of statistics has certain limitations. One can only apply this when having measured data. On the other hand, it can be applied to a large population of data when the sample data is representative of the population
- The descriptive form of statistics is almost always 100 percent accurate as no assumptions are being made for the raw population data. Whereas, inferential statistics are based on the speculations or conclusions based on samples. That is why one cannot find a 100% accuracy in inferential statistics.
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Use of Statistical Software in Data Analysis
Statistical software is a tool used to statistically analyze data obtained during a study. Data analysis becomes much easier with such applications. Many academics use this program to save time while increasing accuracy.
Let’s see how this program consistently outperforms manual data analysis:
Reduction in the chance of error in sampling
Research is only successful if the data gathered is appropriately examined. If data is obtained via improper processes, it will yield meaningless and inaccurate results. Sampling error occurs when there is a disparity between the real and sample populations.
Reduce workload
When a person employs statistical software, they may access a wider database and make easy adjustments. The program also reduced the workload. Most of the software is automated. You do not need to manually enter the data again and over.
Simplified solutions with accurate results
Statistical analysis can provide easy solutions to complicated problems or questions. This might be useful when dealing with limited data. However, when dealing with vast volumes of data, simple techniques might lead to inaccurate analysis.
Helps in fetching precise results
Using statistical software aids in the generation of simple solutions as well as precise findings. The program includes capabilities such as multivariate analysis, regression analysis, statistical process control, and others to make data management easier. These features ensure that critical data is kept safe and that outcomes are straightforward to interpret.
There are additional benefits to using different statistical tools throughout the data analysis process.
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Colleges to Get a Degree in Statistics
There are many colleges around the USA and other parts of the world from where students can get a statistics degree. These courses are offered both online as well as in the conventional manner.
Let’s take a quick look at some of the most popular colleges where students prefer to obtain a statistics degree.
Colleges in the USA:
- Colorado State University
- North Carolina State University
- Grand Canyon University
- Southern New Hampshire University
- University of Delaware
- Capitol Technology University
In the UK
- Imperial College London
- University of London
- Queen Mary University of London
- Oxford University
In Australia and New Zealand
- Australian National University
- Macquarie University
- RMIT University
- The University of Melbourne
- University of Southern Queensland
- University of Otago
- Massey University
- University of Canterbury
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Job Opportunities in The Field of Descriptive and Inferential Statistics
Job prospects are always on the higher side for statistics. The subject being a highly demanding one, students can get several options when pursuing and completing a degree in this it.
Descriptive and inferential statistics are among the sub-categories and students can find highly-paying job opportunities when they are reaching out to employers with a degree in any of the subjects.
Following are some of the job profiles students can think of when having a degree in descriptive and inferential statistics:
- Manufacturing industry
- Marketing businesses
- Engineering firms
- Companies or businesses dealing with statistical computing
- Educational sector
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Conclusion
Statistics and specifically its sub-categories descriptive and inferential statistics has many opportunities to offer. These two are highly theoretical bases and need crystal clear ideas for effective application. Once students can show their excellence in these fields, they can surely get good results and have a high-paying job in any part of the world to make and build a better world and a good future.
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