We all are well aware of statistics as a tool of collecting data and helping us understanding patterns managing data and handling all the quantitative units. It gives us a quantitative brief about the data we are getting the hold of and thus help numerous data analysts with numerous statistical methods.

These statistical methods are divided into main branches i.e.

1. Descriptive Statistics: the descriptive statistical method is defined as the focus developed on the visible characteristics of a dataset. I.e. a dataset contains a lot of characteristics but this method just focuses on the visible characteristics. For ex. A sample or a population. This method is also widely known as summary statistics. The reason for the widespread usage of descriptive analysis is its qualities describing the insights about a data that has been collected whether it is from a sample or a set of population. It helps data analysts to measure the looks of a descriptive statistical data by the 3 main factors:
• Distribution- it stands for the frequency of different outcomes in a population or a sample. It can be shown in a numerical tabular presentation or in a pie chart form.
• Central Tendency- the measurements that look at the central measurement value, you might remember taking out mean, median and mode out of a dataset. These three are the major branches of central tendency.
• Variability- the spreading out of values, the dispersion of values as well as measuring how the values are distributed accounts to the variability. Just like the central tendency it just a term used to describe the deviation from â€˜xâ€™ to â€˜yâ€™.

These terms shall not be confused with a inferential statistics technique as there is a clear difference between descriptive and inferential statistics.

1. Inferential statistics: this type of statistical data collection is entitled to make generalizations about a larger population based on the representative sample featured on the dataset. As this method is majorly used and focused on making certain predictions about a sample pertaining to a larger population.Â  This is the reason that inferential data cannot be accurate as it is dependent on the sample of the population collected. A data is collected randomly by selecting a set of people from a population and that sample population or people are responsible for the predictions made for the entire population. The implications still believe that the random selecting of people is the best way to choose out the sample population and make further predictions on them.

The expert professionals of BookMyEssay providing academic writing guidance have laid down the difference between the two statistical methods, explaining the techniques as well as the core structure and usage by data analysts of the two.

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