Data Analysis and Interpretation

Learning Objectives

Distinguish between qualitative and quantitative data.

Define accuracy and precision in the context of measurement.

Identify independent and dependent variables on a graph.

Explain the difference between correlation and causation.

Making Sense of Data

Science is not just about performing experiments; it is also about analyzing and interpreting the data they produce.

Types of Data

Quantitative Data: Data that can be measured numerically. It deals with numbers.

Examples: Temperature in degrees Celsius, length in meters, time in seconds.

Qualitative Data: Data that is descriptive and conceptual. It deals with qualities and characteristics that cannot be easily measured.

Examples: Color, texture, observations of behavior (e.g., 'the solution turned cloudy').

Accuracy vs. Precision

These two terms are not synonymous in science.

Accuracy: How close a measurement is to the true or accepted value. An accurate measurement has low systematic error.

Precision: How close multiple measurements of the same thing are to each other. A precise measurement has low random error.

It is possible for measurements to be precise but not accurate (e.g., a miscalibrated scale that gives the same wrong weight every time). The goal is to be both.

Graphing

Graphs are a crucial tool for visualizing relationships in data.

Independent Variable: The variable that you manipulate or change. It is plotted on the x-axis (the horizontal axis).

Dependent Variable: The variable that you measure in response to the change. It is plotted on the y-axis (the vertical axis).

Correlation vs. Causation

This is one of the most important and misunderstood concepts in data interpretation.

Correlation: A mutual relationship or connection between two or more things. When one variable changes, the other tends to change in a predictable way (either in the same or opposite direction).

Causation: An action or occurrence that can cause another. One event is the result of the occurrence of the other event; there is a causal relationship.

The Key Idea: Correlation does not imply causation. Just because two variables are correlated does not mean that one causes the other. There could be a third, unobserved 'confounding' variable that is causing both.

Example: Ice cream sales are strongly correlated with the number of drownings. This does not mean eating ice cream causes drowning. The confounding variable is hot weather, which causes people to both buy more ice cream and go swimming more often.

Key Terms

Quantitative Data: Data expressing a certain quantity, amount or range. Usually, there are measurement units associated with the data.
Qualitative Data: Data that is descriptive and non-numerical in nature.
Accuracy: The closeness of a measured value to a standard or known value.
Precision: The closeness of two or more measurements to each other.
Correlation: A mutual relationship or connection between two or more things. It does not necessarily imply that one causes the other.