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12 Dec 2022

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Descriptive and inferential statistics are the two major categories into which all statistical techniques fall. This article examines the distinctions between the two and how they affect the data analytics industry.

Let's first discuss the topic of statistics before delving further into these two types.

Every facet of the data is dealt with by the branch of statistics. In order to properly create the findings, it is important to use the right method for gathering the data and to use those samples in the right analysis procedure. In short, statistics is an important process that aids in decision-making based on facts.

Statistics is the branch of applied mathematics that deals with gathering, structuring, analysing, deriving, and presenting data.

The different types of data in statistics are mainly divided into four areas:

**Nominal data**: One category of qualitative information that aids in labelling variables without providing a numerical value is nominal data. It cannot be measured or ordered.

However, both qualitative and quantitative data can be present at times. Nominal data examples include letters, symbols, words, gender, etc.

**Ordinal data**: A type of data called an ordinal variable or data follows a natural order. The important characteristic of nominal data is that there is no established difference between the data values. This variable is primarily present in questionnaires, financial and economic surveys, and other similar contexts.**Continuous data**: Computable data is continuous data. Within a predetermined range, there are an endless number of possible values that can be chosen.**Discrete data**: Only discrete values are possible for discrete data. For discrete information, there exists a finite range of feasible values. These ideals cannot be meaningfully differentiated. Things can be tallied in entire numbers in this place.

Each of these data types is essential to the data analytics process. In truth, data analytics and statistics are very similar. The statistical analysis of a given dataset or datasets is what we mean when we use the phrase "data analytics."

The different statistical categories cover everything from science and psychology to marketing and medicine. But how do you differentiate between descriptive vs inferential statistics?

Descriptive statistics describe a dataset's readily evident features (a population or sample). In contrast, inferential statistics concentrates on drawing conclusions or generalisations from a sample of data in a larger dataset.

Brief informative coefficients known as descriptive statistics are used to sum up a particular data set, which may be a sample of a population or a representation of the complete population. Measurements of central tendency and measures of variability make up descriptive statistics (spread). The mean, median, and mode are measurements of central tendency, while the standard deviation, variance, minimum and maximum variables, kurtosis, and skewness are measures of variability.

Descriptive statistics can be used to describe a population as a whole or a specific sample. Since descriptive statistics merely serve as an explanation, they need to be more focused on the between data types.

Some of the significant types of descriptive statistics are:

- Distribution
- Variability
- Central tendency

We've now explained the highlights of descriptive statistics. In contrast, inferential statistics concentrate on concluding a broader population from a sample representative of that group.

Results from inferential statistics typically take the form of probabilities because their main goal is to make predictions rather than state facts.

The precision of inferential statistics largely depends on the accuracy and population representation of the sample data. To do this, a random sample must be collected.

There is always the implication that random sampling leads to better outcomes. Conversely, conclusions drawn from skewness or kurtosis samples are typically disregarded. Random sampling is essential for applying inferential techniques.

Objectives of descriptive statistics:

- Specify the characteristics of populations or samples.
- Organising and presenting material purely based on facts
- Visually displaying the results by utilising tables, charts, or graphs
- Using the information at hand and drawing conclusions.
- Utilize metrics such as variance, distribution, and central tendency

Objectives of inferential statistics:

- To conclude bigger groups using samples.
- Helps to estimate and forecast future events
- Gives probabilities as the form of the final outcomes
- Extrapolates conclusions from the information at hand
- Utilizes methods like regression and correlation analysis, confidence intervals, and hypothesis testing.

For a more detailed understanding, please go through the following applications:

Let's say we wish to describe the test results for a particular class of 30 pupils. We keep track of every test result and generate the summary data.

The result comes out as: mean value = 79.18

The range varied between 66.21 and 96.53, while the class value of proportion came as 86.7% (>70%).

The distribution is uniformly centred on the mean in this case, where the scores fall between 66.21 and 96.53. It's acceptable to pass the exam with a score of at least 70. According to the report, 86.7% of the pupils have passing grades.

This information provides a realistic portrait of this class when seen as a whole. There is no question about these numbers because we acquired the grades for every student in the class. We cannot apply these findings to a larger student group, though.

In the above instance, when the scores range from 66.21 to 96.53, the distribution is uniformly centred on the mean in descriptive statistics. With a score of at least 70, you can pass the test. The data indicates that 86.7% of the students achieved passing grades.

This information provides a generally realistic portrait of this class when seen as a whole. However, we cannot generalise these results to a larger student population.

Let's use eighth-graders attending public schools in Pune as our population definition. To ensure a representative sample, we must develop a random sampling strategy. This procedure may be difficult.

Assume for this illustration that we are given a list of names for the entire population and that we randomly select 100 students from that sample to get their test results. Be aware that these pupils will not be from a single class but rather a variety of classes from various schools throughout the city.

We can say with 95% certainty that the population mean between 77.4 and 80.9 despite the ambiguity surrounding these values.

The population standard deviation, the dispersion indicator, is most likely to range between 7.7 and 10.1.

Additionally, 77% and 92% of the population are predicted to receive satisfactory results with the help of inferential statistics.

It is easier to analyse the trend of a small group with descriptive statistics. However, you must use inferential statistics if you require proof that an impact or relationship between variables occurs in the entire population instead of just your sample.

Do you want to learn core statistics concepts and test your skills through industry-level projects? Then consider enrolling on Skill-Lync’s PG program on Statistics and Probability for Data Sciences.

Author

Anup KumarH S

Author

Skill-Lync

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