Differences between Predictive Analytics vs Statistics
Predictive Analytics vs Statistics is the comparison between two techniques that are used for data analysis. Predictive Analytics helps to predict the futuristic value or the outcomes based upon the past and present data set. Whereas statistics is the mathematical computation of data for analyzing, interpreting and identifying the correlations. Predictive analytics depends upon the advanced machine learning algorithms such as regression and classification for generating predictive data models. Statistics uses basic mathematical formulas and concepts such as identifying mean, median, mode, hypothesis testing, variance, and standard deviation calculation to identify data distributions. Predictive analytics are implemented based on statistical analysis.
Head to Head Comparisons Between Predictive Analytics and Statistics (Infographics)
Below is the Top 6 Comparison between Predictive Analytics and Statistics:
Key Differences Between Predictive Analytics and Statistics
Below is the list of items, explain the differences between Predictive Analytics and Statistics:
- Predictive Analytics is used to make predictions about unknown future events. Whereas statistics is the science and it’s mainly used in ‘Research’. Statistics helps in making a conclusion from the data by collecting, analyzing and presenting.
- For a business to bloom, it must collect and generate facts that reflect its current status. Statistics helps these facts or data to be changed into information, in order to support rational management decision making.
How it works:
•In Predictive Analytics, predictive models use known results to develop or train a model that can be used to predict values for different or new data. This modeling provides results in the form of predictions that represent a probability of the target variable based on estimated importance from a set of input variables.
•Statistics summarizes the data for public use. There are two main statistical methods: Descriptive Statistics and Inferential Statistics.
- Descriptive Statistics: It summarizes the data from a sample using indexes such as mean or standard deviation.
- Inferential Statistics: It draws the conclusions from the data that are subject to random variation such as observation errors and sample variation.
•Predictive Analytics includes Data Collection, Data Modeling, and Statistics.
•Predictive models play a vital role in predictive analytics. There are two types of predictive models.
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- Classification models
- Regression models
- Popular method in statistics and works for predictive analytics too.
• Predictive Analytics is not single; it includes and depends on algorithms and methodologies. Examples are Regression models, Time series analysis etc.
• Statistics help, analyst, to build the predictive model to foresee the results or business, so it typically comes under the domain of data science, statistical analysis, and other skilled data analysis.
• In both Predictive Analytics and Statistics, data engineers help to gather relevant data and prepare it for analysis. In a way, statics acts as an input data source for predictive analytics.
• Once the data collection has occurred, a statistical model is formulated, trained and modified as needed to produce accurate results. The model is then run against the selected data to generate predictions
• Let’s take real-life examples or scenarios to understand them better. Some of the popular examples are Weather forecast, Trading, Healthcare, and Retails.
• In reality, it’s all about finding patterns in a vast amount of data. Applying the right statistical models allows you to gain insights from the information at your disposal. The hidden patterns unveiled by the process makes it possible to make predictions.
• Let’s look at once scenario to get the inside picture of how statistics and predictive analytics guessing the future events.
• Big companies are using predictive analytics. For example, open your Amazon site and take a look around the site. A huge percentage of the screen is devoted to “recommended” products, and each recommendation area is a slightly different predictive algorithm based on different data.
Predictive Analytics and Statistics Comparison Table
Below is the Comparison Table which explains the differences between Predictive Analytics and Statistics.
| Predictive Analytics | Statistics | |
|
Definition |
Predictive analytics is a branch of the data analytics to predict the future events. | Statistics in simpler terms is a collection of numerical facts. It is the science of collecting, classifying and representing the numerical data. |
|
Why it matters? |
Predictive analytics can identify the risks and opportunities for the future.
By using Predictive analytics, the business can effectively interpret big data for their benefits.
|
Statistics are important for researchers, analyzers, and business.
|
|
Relation |
It involves applying statistical analytics techniques to predict the future. | Statistics and predictive analytics work together to make good decisions for future. |
|
Methods/ Techniques |
The predictive analytics software relies heavily on advanced algorithms and methodologies
|
Some of the Statistics techniques are
|
|
Uses / Fields |
Using the information from predictive analytics can help companies and business applications.
|
Statistics can be used in many research fields.
|
|
Branches |
Predictive analytics is one of the types of Data Analytics. The other analytics are descriptive and prescriptive analytics. | The two main branches of statistics are descriptive statistics and inferential statistics. |
Conclusion
Predictive Analytics and Statistics are used to analyze current data and historical data to make predictions about future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning and artificial intelligence.
Predictive analytics requires a high level of expertise with statistical methods and the ability to build predictive data models. So we can conclude that both works together to draw the conclusions and predictions from the data.
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