1. Lessons
  2. Gr12
  3. Math
  4. Data Management
  5. Critical Analysis

Introduction to Critical Analysis

Critical analysis is the ability to evaluate information objectively and identify hidden influences that shape perceptions. It is crucial in media literacy, research, and decision-making.

In order to properly use Critical Analysis in Data Management, You should be particularly careful about accepting statistical evidence from sources that could be biased. For example, Lobby groups and advertisers like to use statistics because they appear scientific and objective, when in reality statistics from such sources are potentially flawed by unintentional or, occasionally, entirely intentional bias.

The Media is also guilty of doing this too; newspapers and radio and television news programs often run stories involving statistics. The media is reasonably careful about how they present statistics; For example, they often commission election polls or surveys on major issues. However, their reporters and editors often face tight deadlines and lack the time and mathematical knowledge to thoroughly critique statistical material, and thus could produce mathematical inconsistencies or false information.

Hidden Variables

You should also be mindful of Hidden Variables, which are factors that influence an outcome but are not immediately obvious. Hidden variables can lead to misleading conclusions if not accounted for.

Here is how to find Hidden Variables:

  • Look beyond surface-level correlations. Does A truly cause B, or is there another factor?
  • Examine external influences. Consider economic, social, and environmental factors.
  • Compare multiple sources. Different datasets and perspectives may reveal hidden influences.

A study finds that students who take handwritten notes score higher on tests than those who type. What could be a hidden variable influencing this result?

There could be many hidden variables affecting this data. For example:

  • Engagement Level
  • Retention and Learning Style
  • Class Participation
  • Distractions

The main idea is to look out for variables that aren't explicitly mentioned, but could affect the result nontheless.

Intentional vs. Unintentional Bias

In the data you get information from, there could be Bias. This would be in the data either intentionally or unintentionally.

  • Intentional bias is purposefully manipulating information to influence opinions. An example of this would be political campaign ads that only highlight an opponent's failures
  • Unintentional bias occurs due to personal beliefs, assumptions, or flawed data collection/awareness, rather than a deliberate attempt to deceive. An example of this would be a survey on work-life balance that only includes responses from office workers, excluding freelancers and remote employees.

A researcher wants to determine if social media harms mental health. They survey only people who actively use social media. Is this an example of intentional or unintentional bias?

This is an example of unintentional bias. The researcher doesn't mean to sway the result, but in only interviewing consumers, they leave out a significant portion of society. Interviewing the portion of non-social media users could've change the result.