Introduction

The purpose of graphing in science is to examine the relationship between variables.A good hypothesis predicts how the two variables are related; a graph of the experimental data can help support or disprove the hypothesis. The relationship between variables can be complicated.

While all graphs have numbers and labels to identify the data, there are many different kinds of graphs. The five most common types of graphs are: line graph, scatter plot, bar graph, histogram, and pie chart. Choosing the best type of graph to display your data is critical to helping you finds trends and make new discoveries. Using the wrong type of graph may hide the story your data is telling.

Additionally, there are many ways we can be fooled or mislead by a graph. Some common misleading items we need to watch out for include:

• scales that do not begin at zero;
• scales made very small to make the graph look very big;
• scale values or labels missing from the graph;
• incorrect scales placed on the graph;
• pieces of a Pie Chart that are not the correct size;
• oversized volumes of objects that are too big for the vertical scale differences they represent;
• size of images used in Pictographs being different for the different categories being graphed;
• and, graphs being a non-standard size or shape

QUESTION:

Several ways in which we can be fooled or mislead by a graph were identified in the Lab 3 Introduction. Paste two examples of misleading graphs below. Be sure to identify why each graph is misleading.