Lesson Notes By Weeks and Term v5 - Grade 12

Lesson Video

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Performance objectives

• This week, we delve deeper into the critical skill of analyzing data presented in reports, graphs, and media. In today's world, we are constantly bombarded with information, often presented visually or summarized in reports. Being able to critically evaluate this information is crucial for making informed decisions about our lives, from understanding election results and economic trends to interpreting health advice and advertising claims. In a South African context, where access to reliable information can be challenging and misinformation can have serious consequences (e.g., related to social issues, political discourse, or public health), this skill is particularly vital for active citizenship and informed participation in society.
• This week builds on basic data interpretation, moving towards a more evaluative approach. We'll focus on identifying potential biases, misleading representations, and inconsistencies in data presentation. We will consider the source of the data and the potential motivations behind how it is presented.
• By the end of this week, you will be able to:
• Objective 1: Identify and explain common methods used to misrepresent data in graphs and reports (e.g., manipulated axes, selective reporting, inappropriate graph types).
• Objective 2: Critically evaluate the source of data in a report or media article, considering potential biases or conflicts of interest.

Lesson summary

This week, we delve deeper into the critical skill of analyzing data presented in reports, graphs, and media. In today's world, we are constantly bombarded with information, often presented visually or summarized in reports. Being able to critically evaluate this information is crucial for making informed decisions about our lives, from understanding election results and economic trends to interpreting health advice and advertising claims.

Lesson notes

2.1 Methods of Misrepresentation: Data can be misrepresented in various ways, either intentionally or unintentionally. Recognizing these techniques is the first step to becoming a critical consumer of information.

Manipulated Axes: This is a common tactic in graphs. Changing the scale of the y-axis (usually the vertical axis) or starting the axis at a value other than zero can exaggerate or diminish the perceived differences in the data. A small change in the data can appear significant, or a large change can appear insignificant.

Example:* Imagine a graph showing the increase in electricity prices over the past 5 years. If the y-axis starts at R2.00 per kWh instead of R0.00, the increase will appear much more dramatic than it actually is. This could be used to justify further price increases.

Selective Reporting: This involves only presenting data that supports a particular viewpoint while omitting data that contradicts it. This is a form of bias.

Example:* A company promoting a new energy drink might only report the positive results from a clinical trial (e.g., increased alertness) while ignoring or downplaying any negative side effects (e.g., increased heart rate, anxiety).

Inappropriate Graph Types: Using the wrong type of graph can distort the data or make it difficult to understand.

Example:* Using a pie chart to compare multiple sets of data over time is generally inappropriate. A line graph or bar chart would be much clearer. Similarly, using a 3D graph can distort the proportions and make it difficult to accurately compare values. Correlation vs.

Causation: Just because two things are correlated (happen together) doesn't mean that one causes the other. Misrepresenting correlation as causation is a common fallacy.

Example:* A study might show a correlation between ice cream sales and crime rates.

However, it's unlikely that eating ice cream causes crime. More likely, both ice cream sales and crime rates increase during warmer weather.

Cherry-picking data: Selecting only the data points that support a desired conclusion while ignoring contradictory evidence.

Example:* Citing unemployment rates from a single quarter that show improvement without acknowledging the overall trend of increasing unemployment. 2.2 Evaluating Data Sources: The source of data is crucial.

Consider the following: Credibility: Is the source known for being reliable and accurate? Are they experts in the field? Look for sources that are peer-reviewed or published by reputable organizations.

Bias: Does the source have a particular agenda or viewpoint that might influence how the data is presented? Who funded the research or report? Consider the potential motives of the source.

Conflicts of Interest: Does the source have a financial or other vested interest in the outcome of the data? For example, a study funded by a tobacco company might be less likely to report the negative health effects of smoking. 2.3 Analyzing Claims and Conclusions: Logical Fallacies: Watch out for logical fallacies, such as "ad hominem" attacks (attacking the person making the argument instead of the argument itself), "straw man" arguments (misrepresenting someone's argument to make it easier to attack), and "appeal to authority" (claiming something is true simply because an authority figure says so).

Unsupported Conclusions: Are the conclusions drawn from the data actually supported by the evidence? Does the report overstate the findings or make claims that go beyond what the data can reasonably support?

Sample Size and Representativeness: Is the sample size large enough to draw meaningful conclusions? Is the sample representative of the population being studied? For instance, a survey conducted only among wealthy individuals cannot be generalized to the entire South African population. 2.4 Sampling Methods: Different sampling methods can significantly affect the reliability and generalizability of data.

Random Sampling: Every member of the population has an equal chance of being selected. This is the ideal method, but it can be difficult to achieve in practice.

Stratified Sampling: The population is divided into subgroups (strata), and a random sample is taken from each subgroup. This ensures that the sample is representative of the population in terms of specific characteristics (e.g., race, gender, income).

Convenience Sampling: Participants are selected based on their availability and willingness to participate. This is the easiest method, but it is also the most likely to be biased.

Example:* Surveying shoppers at a single mall about their consumer habits is a convenience sample and may not be representative of the broader population's spending patterns.

Systematic Sampling: Selecting individuals at regular intervals from an ordered list.

Example:* Choosing every tenth person from a voter registration list. 2.5 Comparing Data Representations: The same data can be presented in different ways.