The importance of quality of data cannot be overstated, as it directly impacts the accuracy and reliability of the insights and decisions derived from it. While the importance of quality data cannot be overstated, it is also important to ensure that the data being analyzed is normally distributed. Normal distribution is a statistical term that describes the shape of the data, and it has several key properties that make it ideal for determining the quality of data.
Many businesses struggle with poor-quality data, which can lead to inaccurate insights and decisions. However, even when data is collected with care, it can still be difficult to determine its quality and reliability.
By using normal distribution analysis, businesses can determine the shape of their data and identify any outliers or skewness that may indicate errors or biases in the data collection process. This can lead to more accurate insights and better-informed decisions, ultimately improving business performance and outcomes.
By using normal distribution analysis to determine the quality of their data, businesses can reduce errors and biases, leading to more accurate insights and better-informed decisions. This had lead to improved business performance, increased profits, and better outcomes for customers and stakeholders.
One of the main advantages of normal distribution is that it allows us to determine the outliers or extreme values in the data, which can indicate errors or inconsistencies in the data collection process. Outliers can have a significant impact on the results of the analysis, and by identifying and removing them, we can improve the quality of the data and the accuracy of the analysis.
Normal distribution also allows us to determine the skewness or asymmetry of the data, which can indicate bias or other issues in the data collection process. For example, if the data is skewed to the right or left, it may indicate that there are certain factors or variables that are affecting the data in a disproportionate way, which can lead to incorrect insights and decisions.
Ultimately, using normal distribution to determine the quality of data is important because it allows us to identify errors, inconsistencies, and biases in the data collection process, which can improve the accuracy and reliability of the insights and decisions derived from it. Therefore, it is essential to ensure that the data being analyzed is normally distributed, and to apply statistical techniques to identify and remove outliers and skewness, in order to obtain the most accurate and reliable results possible.
In the below screenshot we have checked whether the data is normally distributed or not using PredictEasy’s Shapiro Wilk test feature, which helps us check the normality of data. When we run the test on a feature (Tool Wear) the result says that the distribution is not Gaussian (Not normal). We can work on the data to improve the normality, hence improving the quality of analysis.
