Top 3 Benefits of Statistical Analysis Software over Manual Analysis
Statistical analysis is the process of collecting, transforming, and organizing data to detect useful information for making a well-informed decision. The statistical analysis gives the business managers real-time data about complex conditions to aid them in making decisions based on facts rather than hunches.
The most common usage of statistics is to measure performance, whether it is the performance of an enhanced marketing tactic, a new line of product or simply the performance of the employees. Moreover, it helps businesses predict and navigate through the risks as well as optimize the return on the investment.
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With more industries opting for the best big data analytics firms to help churn out the data, the need for statistical analysis of data is increasing. The statistical analysis brings in numerous benefits to make the best usage of the vast data available, such as assisting in market research, product development, mapping out the company’s growth rate, improve the efficiency of the company, etc.
However, manually analyzing the data generated by the business can result in misleading conclusions and serious distortions. Businesses must acknowledge the disadvantages to grasp the concept of statistical analysis.
Let us analyze the key drawbacks of data analysis and how statistical analysis software help avoiding them.
- Error in Sampling
The success of statistical analysis majorly depends on the collected data. When a data scientist gathers data based on biased or faulty procedures, it entirely renders the results useless. Sampling error occurs when there is a gap between the actual population and the sample population. A sample that contains a wide array of representations may produce a minor error. However, if the sample population does not co-relate to the focused population, the result can be misleading. For instance, if research focused on finding the most efficient hotel management software includes the majority of participants who work in the retail or health-care industry, the outcome will be evasive. Similarly, if a greater number of participants are from the same group of hotels, the results will be biased.
Statistical analysis software has access to a larger database and offers easy customization. These two attributes combined together allow businesses to ensure that the correct data is assessed and the ensuing figures are unbiased and conclusive. Additionally, the software also analyzes the figures and information pre-existing in the business. As most statistical software are automated, there is no need to input data manually repeatedly, cutting down the workload.
2. Oversimplified Solutions
The statistical analysis produces extremely simple answers to particularly complex questions. This can be considered an advantage when limited data is scrutinized. However, when vast information is under consideration, oversimplified solutions can be deemed as incorrect analysis. For example, studying a multivariate concept with the help of a single variable. The inadequate set of fragments can result in faulty assumptions. There is a wide range of analytical tools, and the chances that the manual calculation may use a tool that is incorrect for the specific situation is extremely high. Moreover, statistical analyses make assumptions that assess the properties of the sample, variables, etc. It is acceptable to waive certain assumptions under specific conditions to simplify the results. However, violating a critical assumption will lead to producing misleading results.
The statistical analysis software understands the need to create solutions that are easy to understand while maintaining the accuracy of the result. The software knows when to assess the group mean or the median as well as whether to assert a linear relationship to data or nonlinear. The best statistical analysis software offers features such as multivariate analysis, regression analysis, statistical process control, etc. to handle the workings of the data. These features control the process to avoid omitting crucial data while simultaneously keeping the result intelligible.
3. Correlation vs. Causation
Statistical analysis tends to overgeneralize the results. At times, the researchers often discover evidence displaying the correlation between two variables. However, this doesn’t commonly transpire that one variable is the cause of another. For instance, a website implemented new web designs; the web page traffic started to increase simultaneously. Now the question is whether the new design is the cause of traffic increase or whether the traffic boost was organic and correlated to the time of the release of the new design? Manual analysis of the data can deduce a nonexistent correlation between two scenarios and can give the decision-maker the wrong ideas or impressions.
To establish cause and effect between two variables, it is important to extensively test the association between a dependent and an independent variable. Statistical analysis software helps businesses make decisions based on accurate predictions. The software employs algorithms and machine learning to identify causation. The software performs hypothesis testing and A/B/n experiments that study the results of randomized experiments in multiple settings to verify that the results represent causality. Understanding the link between crucial outcomes and client’s behaviors is the foundation of generating the product.
Data is the backbone of any business structure in the current age and data mining is of utmost importance. Even when the analysts perform manual data analysis with great caution, the result can be incorrect if the dataset is investigated for too long. With the assistance of statistical analysis software, the statisticians can efficiently track the number of tests that have been performed in the course of study and present the results in the proper context. To sum up, statistical analysis software assists businesses to tread the path carefully and avoid the flaws mentioned above.
Most software for statistical analysis has extremely user-friendly interface that further simplifies the process. It is swift in generating charts and graphs to represent descriptive statistics. Additionally, incorporating statistical analysis software does not require hiring an in-house team of data scientists. However, it is advisable to opt for a free trial of the software before investing to verify its workings.
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