The significance of statistics in research cannot be downplayed since it is the basis on which credible conclusions and recommendations are made. Statistics comes in handy when collecting and analyzing data on a new topic that is being researched on. Most times in research, the goal is to utilize data to forecast on what might occur in the future. Statistics ensures that results and findings are more accurate, believable, and consequently useful. Statistics provide one with a measure of the variables being investigated and an examination of the relationships within the data set. It ensures that an individual can make predictions, test the hypotheses, develop theories, and present the information. Essentially statistics helps an individual draw conclusions about the new topic that is being investigated by using sample results.
Descriptive statistics is more concerned with the quantitative attributes of a data set and the methods utilized in their description. They are used in organizing and describing the characteristics of data that has already been collected. Key examples of descriptive statistics are the mode, mean, and median. Most organizations often have voluminous amounts of numerical data on certain variables and to make analysis easier and accurate, the data set has to be described. The most common value within the data collection is the modal value. The average value is the mean and the centrally located value is the median. Descriptive statistics helps researchers define and create meaning to a large collection of data by representing key characteristics of that data set in a simple and clear structure (SalKind, 2014).
Inferential analytical statistics is utilized when making inferences about populations or samples. Populations are large groups of people or firms within an industry while samples are smaller subsets of populations that are utilized to deduce some conclusion about the entire population. An inference can be an assumption, deduction, or a possibility of a data set. When utilizing inferential statistics, a researcher first creates a hypothesis, which is an assumed conclusion about the data set. After creating the hypothesis, the researcher then investigates the hypothesis by utilizing a sample then inferring her findings to the whole population. Inferential statistics is quite significant because they help individuals arrive at conclusions that are beyond the data set. This is because inferential statistics provides knowledge on qualitative variables like what the population thinks, or even the judgments the population makes.
The mean is the average of values within the data set. It is computed by summing all the values within the data set and then dividing that summation by the total number of values in the group. The sample mean can be said to be the measure of central tendency that represents the population mean almost accurately. The main disadvantage of the mean is that it is very sensitive to extreme scores. The median is the midpoint of the scores found in the data collection. It is the point where half of the scores fall above and below the midpoint. It is found by listing all values in chronological order then finding the middle-most score. When a data set consists of extreme scores, then the median is preferred over the mean as the most accurate measure of central tendency. The mode is the most frequent value in the data set.
The mode is the most general measure of central tendency and the least precise as compared to the mean and the median. However, it plays a very significant role when it comes to understanding the characteristics of special sets of scores. Researchers might want to use the mode instead of the other measures of central tendency when the data set is categorical in nature. This is to mean that the data can be classified into mutually exclusive categories such as nationalism, gender, and location. It is essential to utilize the mode when data can fit into the individual categories of the data set. The mode is quite appropriate for nominal data for instance if a researcher is investigating which food item was frequently purchased in 2011, then it would be essential to use the mode instead of other measures of central tendency.
The frequency distribution is the method of tallying and representing the occurrence of certain scores within a data set. When creating a frequency distribution, the scores have to be classified into group intervals. The frequency distribution table summarizes the frequency of outcomes in the sample. The first step is the definition of the range of intervals and then define the class width to be utilized. After that, you construct a frequency table with two columns that are classes and frequency. You then count the number of times the score appears in the data set and then fill those details in the frequency table. All scores should be included in the frequency table even those scores with zero frequency to facilitate the accurate construction of a frequency polygon (SalKind, 2014).
A histogram is created for generating a frequency distribution table. A histogram is a visual representation of the frequency distribution table. The first step is getting the midpoints of the respective classes by dividing the class intervals by two. Then you draw both x and y-axes where the x-axis is the class intervals while the y-axis is the frequency. There is a need to scale the axes efficiently and in a manner that encompasses all the values of the data sets. Highlight all the classes’ upper limits in the x-axis and the frequencies on the y-axis. A bar or column is then drawn through the respective midpoints of each category that represents the frequency of this category.
From this week’s reading, I learned how learning designs can be diversified into cognitive, behaviorism, and constructivism. Mathematical modeling has always been applied when it comes to textbook design, but their results have always differed from possible solutions to the learning process. By observing the social psychology and engaging cognitively with the learning process, more responsibility is placed on the student’s cognitive systems thereby adding more value to the learning process (Railean, 2014). Therefore, constructivism can be utilized in supporting multiple perspectives and the interpretation of knowledge and reality within the society. Data visualization needs to be based on linear thinking that displays content for evaluation so as to deduce credible conclusions. It is imperative to consider human interface in statistical modeling by analyzing their cognitive skills and behavioral tendencies.
Railean, E. (2014). Toward User Interfaces and Data Visualization Criteria for Learning Design of Digital Textbooks. Informatics in Education.
SalKind, N. J. (2014). Statistics for People Who (Think They) Hate Statistics(5th ed). Thousand Oaks CA Sage Publication.
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