DATA INTERPRETETION

 

                   Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions. The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.Data interpretation is the process of reviewing data through some predefined processes which will help assign some meaning to the data and arrive at a relevant conclusion. It involves taking the result of data analysis, making inferences on the relations studied, and using them to conclude.In other words, it's giving meaning to the collected 'CLEANED' raw data.

               Therefore, before talking about interpreting data, they need to be analyzed first. What then, is data analysis?

 Data analysis is the process of ordering, categorizing, manipulating, and summarizing data to obtain answers to research questions. It is usually the first step taken towards data interpretation.Data interpretation is the final step of data analysis. This is where you turn results into actionable items.

 

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 Data Interpretation Methods

             Data interpretation methods are how analysts help people make sense of numerical data that has been collected , analyzed, presented , aggregated,transformed, and displayed . Data , when collected in raw from , may be difficult to understand, which is why analysts need to break down the information gathered so that others can makes sense of it. There are two main methods for data interpretation : qualitative and quantitative   

 Qualitative Data Interpretation Method

                This is a method for breaking down or analyzing so-called qualitative data, also known as categorical data. This method uses texts, rather than numbers or patterns to describe data.Because qualitative data is collected through person-to-person techniques, it isn't easy to present using a numerical approach.Surveys are used to collect data because they allow you to assign numerical values to answers, making them easier to analyze. If we rely solely on the text, it would be a time-consuming and error-prone process. This is why it must be transformed. 

               Qualitative data is usually gathered using a wide variety of person-to-person techniques, which may be difficult to analyze compared to the quantitative research method.There are 2 main types of qualitative data, namely; nominal and ordinal data. These 2 data types are both interpreted using the same method, but ordinal data interpretation is quite easier than that of nominal data.In most cases, ordinal data is usually labeled with numbers during the process of data collection, and coding may not be required. This is different from nominal data that still needs to be coded for proper interpretation.

Quantitative Data Interpretation Method

                This data interpretation is applied when we are dealing with quantitative or numerical data. Since we are dealing with numbers, the values can be displayed in a bar chart or pie chart.The quantitative data interpretation method is used to analyze quantitative data, which is also known as numerical data. This data type contains numbers and is therefore analyzed with the use of numbers and not texts.

                Quantitative data are of 2 main types, namely; discrete and continuous data. Continuous data is further divided into interval data and ratio data, with all the data types being numeric.Moreover, numbers are easier to analyze since they involve statistical modeling techniques like mean and standard deviation.

Mean is an average value of a particular data set obtained or calculated by dividing the sum of the values within that data set by the number of values within that same set.

Standard Deviation is a technique is used to ascertain how responses align with or deviate from the average value or mean. It relies on the meaning to describe the consistency of the replies within a particular data set. You can use this when calculating the average pay for a certain profession and then displaying the upper and lower values in the data set. 

                                                                                                                                                                    

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Advantages and Importance of Data Interpretation

  • It saves costs by providing costing opportunities 
  • Anticipating needs and identifying trends
  • Data interpretation is important because it helps make data-driven decisions. 

Conclusion  

                  Data interpretation and analysis is an important aspect of working with data sets in any field or research and statistics. They both go hand in hand, as the process of data interpretation involves the analysis of data

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