This document defines key scientific concepts like observation, inference, hypotheses, qualitative and quantitative data, independent and dependent variables. It also discusses when to use bar graphs versus line graphs, with bar graphs used for discrete data that can be counted, and line graphs showing relationships over continuous data. Finally, it lists the components of a successful experiment, including running multiple trials with randomly selected subjects and a large sample size to reduce bias.

1. Observation Information gathered using the 5 senses

2. Inference A Conclusion made on the basis of facts rather than on direct observations

3. Hypothesis An “educated statement” that is testable in the natural world Hypotheses are never “proven” They are “supported” Hypotheses are made to be rejected

4. Qualitative Data Data Described by Certain Qualities Adjectives

5. Quantitative Data Data That Can Be Measured in Numbers

6. Independent Variable The variable that the experimenter changes or manipulates

7. Dependent Variable The variable that the experimenter measures

8. When to use a bar graph Discrete data – each item is a whole and separate unit (cats and dogs) You can’t really have a cat and a half!! Compare sets of data

9. When to use a line graph Graphs most commonly used in science they show a relationship Continuous data – the points could theoretically be subdivided Additional points are always possible Graphing over time, temperature, weight

11. A Successful Experiment Includes Multiple Trials If using subjects, they are chosen at random Large sample size Tries to remove all bias