# Answer the following questions in the space provided. All answers must be in your own words. Briefly describe frequency distributions and provide an explanation of when they are best used. Describe each of the graphs (histogram, polygon, bar graph, stem and leaf) and explain when they are best used. Describe an example of a distribution (in real-life) that is positively and negatively skewed.

## QUESTION

Answer the following questions in the space provided. All answers must be **in your own words**.

- Briefly describe frequency distributions and provide an explanation of when they are best used.

- Describe each of the graphs (histogram, polygon, bar graph, stem and leaf) and explain when they are best used.

- Describe an example of a distribution (in real-life) that is positively and negatively skewed.

- Suppose the Campus Heavy Drinkers Alliance and the Campus Temperance Union have different perceptions of the severity of the alcohol abuse problem on their campus. The Campus Heavy Drinkers Alliance promotes alcohol use, arguing “as much as possible, as often as possible.” The Campus Temperance Union, on the other hand, has campaigned for the prohibition of alcohol on campus, arguing “rarely, if ever.” The student government is considering new campus alcohol policies and has asked both groups to present their assessments of the situation.
- Which graph was presented by each group? Why?

- Discuss the implications of using graphs ‘to your advantage’.

- What understanding does the general public need to know/have in order to accurately interpret data?

- Consider the following set of data:

**20, 25, 25, 36, 42, 56, 69, 74, 75, 85**

** **

- Find the mean ________ median _________ mode _______

- Suppose the score 75 in the data is mistakenly recorded as 88 instead of 75. For the sample with this error, find the mean ____ median _____ mode_____

- Explain the change in each of the measures of central tendency (providing justification for your reasoning)

- Suppose the score 75 in the original sample is inadvertently removed from the sample. For the sample with this score removed find the

mean ______ median ______ mode ______

- Explain the change in each of the measures of central tendency (providing justification for your reasoning).

- Describe, in one paragraph, what you found most beneficial in this week’s content.

## ANSWER

**Understanding Frequency Distributions and Graphs in Data Analysis: A Comprehensive Overview**

**Introduction**

In the realm of data analysis, frequency distributions and graphs play a pivotal role in organizing, summarizing, and visualizing data. They offer valuable insights into the patterns, trends, and characteristics of a dataset, enabling researchers, analysts, and decision-makers to make informed conclusions. This essay will delve into the concept of frequency distributions, explore various types of graphs such as histograms, polygons, bar graphs, and stem-and-leaf plots, and discuss their applications in different scenarios. Additionally, we will examine skewed distributions, the implications of using graphs to one’s advantage, the importance of data literacy for accurate interpretation, and the impact of altering data on measures of central tendency.

**Frequency Distributions**

Frequency distributions provide a condensed representation of data by categorizing it into distinct groups and showcasing the frequencies within each category. They are particularly useful when we aim to discern the distribution and patterns of values within a dataset (Manikandan, 2011). By presenting the frequencies visually, we can identify common values, outliers, or prevailing trends, aiding in deeper data understanding and analysis.

**Graph Types and Their Applications**

Histograms: Histograms are graphical representations that use bars to depict the frequencies or relative frequencies of different intervals or bins. They are effective when visualizing the shape and distribution of continuous data. Histograms excel in capturing the range of values, identifying outliers, and assessing the overall distribution pattern within large datasets.

Polygons: Polygons, also known as line graphs, are suitable for illustrating the distribution of continuous data across a range of values. They connect data points representing frequencies using line segments, resulting in a smooth curve. Polygons are valuable in identifying trends, patterns, or changes over time, enabling researchers to discern fluctuations or correlations in the data.

Bar Graphs: Bar graphs utilize rectangular bars to represent the frequencies or relative frequencies of categorical or discrete data. They provide a clear and concise visualization of data distribution, making them useful for comparisons among different categories. Bar graphs offer an intuitive way to interpret data, especially when dealing with non-numerical variables or discrete datasets.

Stem-and-Leaf Plots: Stem-and-leaf plots organize data in a tabular format, separating each data point into a stem (leading digit/s) and a leaf (trailing digit/s). These plots are particularly suitable for smaller datasets, offering a quick overview of the data’s distribution and individual values while preserving the original data. Stem-and-leaf plots assist in identifying central tendencies and outliers, aiding in exploratory data analysis.

**Positively and Negatively Skewed Distributions**

A real-life example of a positively skewed distribution can be observed in the distribution of income within a population. In many societies, a majority of individuals fall within lower income brackets, while a small proportion earns significantly higher incomes, creating a long tail on the right side of the distribution (J. Chen, 2023). Conversely, a negatively skewed distribution could be seen in the distribution of grades in a difficult exam. Most students may score relatively high marks, resulting in a left-skewed distribution, with a small number of students achieving low scores.

**Implications of Using Graphs to Your Advantage**

Using graphs to your advantage involves strategically selecting and presenting data to emphasize specific aspects or support particular arguments. While graphs are valuable tools for data communication, it is crucial to exercise caution and ethical responsibility. Misleading or distorting graphs can lead to misinterpretation, biased conclusions, or the manipulation of public opinion (In & Lee, 2017). Transparency, accuracy, and ethical considerations should always guide the use of graphs to ensure integrity and promote informed decision-making.

**Data Interpretation and the General Public**

Accurate interpretation of data requires a certain level of data literacy from the general public. Individuals should possess fundamental knowledge of key statistical concepts, such as measures of central tendency (mean, median, mode), variability, and graphical representations. Additionally, critical thinking skills, awareness of potential biases, and the ability to contextualize data are vital to avoid misinterpretation or misuse of information. Promoting data literacy among the public is essential for fostering data-driven decision-making and avoiding misinformation.

**Measures of Central Tendency – The Given Dataset**

Mean: (20 + 25 + 25 + 36 + 42 + 56 + 69 + 74 + 75 + 85) / 10 = 47.7

Median: The middle value is 56, as the data is already sorted.

Mode: No value appears more than once, so there is no mode.

**Measures of Central Tendency – Data with Mistaken Recording**

Mean: (20 + 25 + 25 + 36 + 42 + 56 + 69 + 74 + 88 + 85) / 10 = 49.9

Median: The middle value is 56, as the data is already sorted.

Mode: No value appears more than once, so there is no mode.

**Changes in Measures of Central Tendency**

The mean increases from 47.7 to 49.9 due to the higher value (88) replacing the original value (75). The median remains unchanged as it is unaffected by extreme values or changes in data. The mode remains the same since there are no repeated values in the dataset.

**Measures of Central Tendency – Data with Removed Score**

Mean: (20 + 25 + 25 + 36 + 42 + 56 + 69 + 74 + 85) / 9 = 46.1

Median: The middle value is 56, as the data is already sorted.

Mode: No value appears more than once, so there is no mode.

**Changes in Measures of Central Tendency**

The mean decreases from 47.7 to 46.1 due to the removal of the original value (75). The median remains unchanged as it is unaffected by the presence or absence of specific values. The mode remains the same since there are no repeated values in the dataset.

**Conclusion**

This essay has explored the fundamental concepts of frequency distributions and various types of graphs, their applications, and the importance of data interpretation and literacy. We have examined real-life examples of skewed distributions and the implications of using graphs strategically. Additionally, we have analyzed the changes in measures of central tendency when data is altered or manipulated. By gaining a comprehensive understanding of these topics, individuals can navigate data analysis more effectively, make informed decisions, and contribute to a data-literate society.

**References**

Chen, J. (2023). Skewness: Positively and Negatively Skewed Defined With Formula. *Investopedia*. https://www.investopedia.com/terms/s/skewness.asp

In, J., & Lee, S. (2017). Statistical data presentation. *Korean Journal of Anesthesiology*, *70*(3), 267. https://doi.org/10.4097/kjae.2017.70.3.267

Manikandan, S. (2011). Frequency distribution. *Journal of Pharmacology and Pharmacotherapeutics*, *2*(1), 54–56. https://doi.org/10.4103/0976-500x.77120

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