Groups often get so involved with the data collection phase of indicator develop

Groups often get so involved with the data collection phase of indicator development that they forget to spend time discussing what they plan on doing with the data once they have collected it (i.e., develop an analysis plan). Initially, it might be useful to analyze the data with descriiptive statistics to determine how closely the data reflect normal distributions. This is particularly helpful if you have collected a sample and you are curious to have looked at measures of central tendency and measures of dispersion, you are ready to move on to control chart analysis.
What descriiptive statistics will you use?
Measure of Central Tendency Measures of Dispersion
_______ Mean _______ Minimum
_______ Median _______ Maximum
_______ Mode _______ Range
_______ Standard Deviation
Why did you select the measure(s) of central tendency and measure(s) of dispersion chart over the other possibilities?
Descriiptive statistics refers to the process of analyzing, summarizing, and presenting findings that are interconnected to a data set that is retrieved from a sample of a population or the whole population. Descriiptive statistics aid in the facilitation of the visualization of data. Descriiptive statistics paves the way for data representation in meaningful ways and allows for better comprehension (George & Mallery, 2016). This factor, in turn, allows for a better, concise, and simple interpretation of the data set under scrutiny. The primary reason that I selected central tendency and measures of dispersion over other analytical methods in this study is that the methods allow for extensive scrutinization of the data collected.
Central tendency is a descriiptive summary of a dataset by utilizing a single data value, which allows reflection of the median of the distribution of data. The measures of central tendency are also referred to as measures of a central point. The measures of central tendency encompass the mean, median, and mode. Mean is the most common value in the measure of central tendency, and it is utilized in measuring the average common in a data set (Health Research & Educational Trust, 2016). Median refers to the middle score of a data set arranged chronologically in ascending order.
Measures of dispersion, also referred to as measures of variability, allow a researcher to comprehend better the extent to which the values of the responses are spread out. Firstly, the range gives a researcher a vague idea of how far apart the most extreme replies are. Standard deviation refers to the average amount of variability in a data set. Standard deviation informs a researcher of the extent to which each score lies from the average total (George & Mallery, 2016). The higher the standard deviation, the more variability in the dataset. Variance refers to the average of squared deviations from the mean. Variance illuminates the extent to which the data is spread. If higher the spread of data, the bigger the variance concerning the mean.
There are various reasons that I selected measures of central tendency and measures of dispersion over other descriiptive statistics methods. Measures of central tendency are crucial because they inform us of where the central location of a data set is, identify where most of the data lies, illustrate the skewness of a data set, whether positive or negative. By plotting a distribution graph, it is possible to detect the outliers from the data. Unlike other methods, central tendency allows a researcher to find out the behavior of data. Also, any individual analyzing data must comprehend what typical and varied data are present. Measures of dispersion are crucial in research because they can show a researcher within a particular time or a group of people. Dispersion is crucial in samples because it identifies the margin of error that will be present when making inferences about measures of central tendency, such as averages.