Question

concept 1: nature of science
objectives:

1. define science.
2. give an example of a hypothesis in the correct format.
3. explain the relationship between independent and dependent variables within a hypothesis.
4. differentiate between an observation and an inference.
5. explain the difference between accuracy and precision, and the significance of having data that is both.
6. list the general steps in designing and conducting an experiment.
7. give an example of a scientific investigation design, with appropriate constants and variables (iv and dv).
8. explain the general steps of the technological design process, and the criteria that must be considered when designing a solution.
9. be able to conduct a scientific experiment using appropriate laboratory equipment and making precise measurements.
10. be able to organize data from an experiment in a chart, table, or graph, and interpret it.
11. analyze data with respect to a hypothesis and draw an appropriate conclusion.
12. be able to select the most appropriate hypothesis and identify variables when given the description of a scientific investigation.

Explanation:

1. Science is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe.
2. Example of a hypothesis in correct format: "If plants are exposed to more sunlight (independent variable), then they will grow taller (dependent variable)."
3. In a hypothesis, the independent variable is the factor that is changed or manipulated by the researcher, and the dependent variable is the factor that is measured and is expected to change as a result of the independent - variable manipulation.
4. An observation is a direct perception of a phenomenon using the senses or scientific instruments. An inference is a conclusion drawn from observations.
5. Accuracy refers to how close a measurement is to the true or accepted value. Precision refers to how close repeated measurements are to each other. Having data that is both accurate and precise is important for reliable scientific results.
6. General steps in designing and conducting an experiment: formulating a question, making a hypothesis, designing the experiment (including identifying variables and constants), conducting the experiment, collecting data, analyzing data, and drawing conclusions.
7. Example of a scientific investigation design: Testing the effect of different amounts of fertilizer (IV) on plant growth (DV), with constants like plant type, pot size, and amount of water.
8. General steps of the technological design process: identify a problem, research the problem, generate possible solutions, select the best solution, develop a model or prototype, test and evaluate the solution, and communicate the results. Criteria to consider include functionality, safety, cost - effectiveness, and environmental impact.
9. To conduct a scientific experiment using appropriate laboratory equipment, first, select the correct equipment based on the experiment requirements. Then, follow proper procedures for measurement, such as zeroing scales and using pipettes accurately.
10. Organizing data in a chart, table, or graph helps in visualizing patterns and trends. For example, a line graph can show the relationship between two variables over time.
11. Analyzing data with respect to a hypothesis involves comparing the collected data to what was predicted in the hypothesis. If the data supports the hypothesis, the conclusion can be in favor of the hypothesis. If not, the hypothesis may need to be revised.
12. When given a scientific investigation description, identify the proposed relationship to select the most appropriate hypothesis. Then, identify the variable being manipulated (IV) and the variable being measured (DV).

Answer:

1. Science is a systematic way of acquiring knowledge about the natural world through observation, experimentation, and analysis.
2. If the amount of sugar in a solution is increased, then the rate of yeast fermentation will increase.
3. The independent variable is what the experimenter changes; the dependent variable changes in response to the independent variable.
4. Observation: Seeing a plant wilt. Inference: The plant wilted because it didn't get enough water.
5. Accuracy: hitting the bull - eye. Precision: hitting the same spot multiple times. Both are needed for reliable data.
6. Question, hypothesis, design, experiment, data collection, analysis, conclusion.
7. Testing the effect of temperature (IV) on the solubility of salt (DV), with the type of salt and volume of water as constants.
8. Steps: problem - identification, research, solution - generation, selection, development, testing, communication. Criteria: functionality, safety, etc.
9. Select proper equipment, follow measurement procedures accurately.
10. Use tables for raw data, graphs for visualizing trends.
11. Compare data to hypothesis; if consistent, support hypothesis; if not, revise.
12. Identify relationship for hypothesis; IV is manipulated, DV is measured.