The process of cleaning, modelling, and transforming data to extract information that can either support or oppose a scientific claim is commonly called data analysis. It is probably the most puzzling part of research that compels students to get enrolled in professional data analysis training. What is the most appropriate method of data analysis for achieving the desired aims is the first question that often encounters students; however, what is the most suitable way to perform a selected data analysis process is the second one. Basically, it is the aims and objective of the research that can hint at the right data analysis method for research. In order to m
ake students aware of two very different types of data analysis- Inferential & Predictive Analysis, this article will highlight their basic concepts along with the differences and similarities between the two.
Inferential analysis:
As far as statistical analysis is concerned, inferential analysis is the major contributor that helps a researcher come to a logical conclusion and make a prediction. As soon as a researcher collects data from the selected sample, he/ she can immediately use inferential analysis to understand the characteristics and features of the larger population based on studying small but best representatives of a population. For inferential analysis, the selection of random and unbiased sampling methods is vital. Otherwise, it may lead to invalid statistical inference. It can be done by using two widely used approaches: making estimates about the population and testing hypotheses to draw generalised conclusions about the whole population.
1.1Making estimates about the population:
The characteristics of a sample or population can be described by statistics and parameters. Statistic describes the sample while parameter explains characteristics of the whole population. Inferential analysis works by measuring the difference between a population parameter and the corresponding statistic. If you are unable to measure the difference, hire best coursework writing services from experts.
1.2 Testing hypothesis to draw the conclusion:
Hypothesis testing is another important statistical analysis categorised under the umbrella of inferential statistics. The overall purpose of this process is to confirm whether a proposed set of hypotheses are supported or nullified by interpreting patterns hidden in the dataset.
Predictive analysis:
Theoretically, predictive analysis is the art and science of using data, machine learning, statistical algorithms, and other artificially intelligent software to know the likelihood of outcomes based on historical data. The purpose of this analysis is to go beyond the typical approach of investigation to unveil reasons for a happening or an even. It aims to give the best assessment of what will happen in the near future. By definition, predictive analysis is the term that analysts use for the type of statistical and modelling techniques useful to make predictions about the future performance or consequences of an event. Mostly, business students’ and entrepreneurs conduct predictive analysis by reviewing the current and historical data to determine repeatable patterns that would likely emerge again. Predictive analytics use a wide range of technologies, especially to predict certain upcoming unknowns. Furthermore, three basic techniques are common to predictive analysis: decision trees, regression, and neural networks.
Decision trees:
Decision-making is the toughest part of predictive analysis; thus, decision trees are important to bring ease to this process. Decision trees are a very simple type of analysis that enables researchers to make a decision by placing variables on different branches of trees. In these trees, branches represent the number of different options available, and leaves represent the end result. In this way, by taking into account the outcomes of all available options, the decision helps researchers to make a better decision.
Regression:
Whenever the relationship between the inputs is linear, or the goals of the research are to identify the repeatable patterns in the data set, regression is a useful statistical analysis tool. Thus, these features of regression help predictive analysts to identify future outcomes as well.
Neutral network:
A neutral network imitates the working principle of the human brain. Pattern recognition and machine learning help such models to achieve aims. The neutral network offers helping hands only if a researcher has to deal with a large data set and when they are unable to find the relationship between inputs and outputs to predict future trends.
Differences between the inferential & predictive analysis:
After reviewing the concepts of inferential & predictive analysis, we are in a position to clearly state the differences between the two. The inferential analysis explores properties using estimates and hypothesis tests; however, predictive analysis focuses on past events and historical data to predict future trends. Predictive analysis is the branch of data analytics that is essential for making useful predictions. In contrast, inferential statistics is the branch of statistical analysis meant for collecting, accessing and interpreting numerical data. Predictive analysis helps in finding the risk and opportunities, while inferential statistical analysis evaluates the credibility and usefulness of different pieces of information. Logistic regression, decision trees, machine learning and artificial intelligence are important techniques for conducting predictive analysis. Contrastingly, the techniques important to inferential statistical analysis includes estimating parameters and testing hypothesis. All in all, the purpose, mode of action, and techniques vital to conducting inferential & predictive analysis all vary greatly.
Similarities between the inferential & predictive analysis:
Like differences, it is important to highlight the similarities between the inferential & predictive analysis as well. As a matter of fact, inferential statistics, unlike descriptive analysis, helps in extracting conclusions that may include predictive hypothesis testing to predict the future. Overall, inferential & predictive analysis can together be used to predict the future outcomes of an event. Guessing the future is the overarching point between inferential & predictive analysis.
Final thoughts:
Consequently, data analysis is critical to all research as it helps researchers in solving puzzles to make sense of different chunks of information. Inferential statistical analysis is one type of data analysis process that extracts conclusions based on the results of descriptive statistical analysis (mean, median, mode, and many more). In contrast, predictive analysis is relatively a new analysis process that uses machine learning, artificial intelligence, and other latest technologies to predict the future. The detailed note on the differences and similarities between the inferential & predictive analysis explained that the number of differences between the two is far greater than the similarities.
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