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Biology Lab Report Reference Guide

Step 1 - PROBLEM . Poor example: What fertilizer is the best?***Clearly state the problem that you are trying to solve. Be as specific as you can.Good Example:Which fertilizer, Miracle Grow or VitaGro is best for growing a particular species of roses?. RESEARCH: Every good scientist spends a great deal of time doing research so that he/she can design experiments that are meaningful and worthwhile. Research is not limited to the written word, but almost always involves communication with othe9448

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Biology Lab Report Reference Guide

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    1. Biology Lab Report Reference Guide

    2. Step 1 - PROBLEM Poor example: What fertilizer is the best? ***Clearly state the problem that you are trying to solve. Be as specific as you can. Good Example:Which fertilizer, Miracle Grow or VitaGro is best for growing a particular species of roses?

    3. RESEARCH: Every good scientist spends a great deal of time doing research so that he/she can design experiments that are meaningful and worthwhile. Research is not limited to the written word, but almost always involves communication with other scientists interested in the same topic. This collaboration is of utmost importance in the research community. You will need to use many references to learn about the problem you are tying to solve.

    4. Possible questions you would need to research about the “Good” problem stated above: What does Miracle Grow contain? What does VitaGro contain? How fast do roses grow? What type of soil do roses like? Has this product ever been used on roses? What kind of nutrients do roses like? Do roses grow best is sun or shade? Do roses grow best is warm or cold climates? What kinds of roses are there? When do roses bloom?

    5. Step 2 - HYPOTHESIS ****Now that you know what your experiment is, what do you think will happen? Poor Example: I think the fertilizer will help the plants. I think that the roses will grow better with fertilizer  Good Example: If I use Miracle Grow instead of no fertilizer, then the flowers with Miracle Grow will produce more flowers compared to the one without miracle grow.

    6. Questions to Consider when Designing an Experiment What organisms will I use? How many organisms should I use? What equipment will I need? How will I set up my experiment? What data will I measure? What will be the manipulative variable? What will be the responding variable? Will I need a control set up? How long will the experiment last? How will I display my data? (data table, graph?)

    7. DETERMINE the VARIABLES: Variables (in general)– the parts of an experiment that could vary or CHANGE. 1. Independent Variable (the cause) Dependent Variable (the effect) Controlled Variables or Constants (stay the same) Controlled Set up vs. Experimental Set up Sample size – determine amount needed for an accurate conclusion

    8. Step 3 – PROCEDURE (& MATERIALS) Make sure that materials are very specific Poor Example : Roses, soil, water, fertilizer Good Example: 1. 150 red rose plants (same species) each 2 feet tall 2. 150 identical 5 gal buckets with 3 holes in the bottom 3. 10 bags of Home Depot brand potting soil (20 lbs.) 4. Miracle Grow fertilizer 5. VitaGrow fertilizer 6. Water 7. Graduated cylinder to measure water 8. Grow lights or green house 9. Yard stick

    9. Procedure Your procedure should be written in such a way that any person using it could get identical results.

    10. Procedure Poor Example: 1.Plant roses, some with fertilizer, some without 2.Water roses 3.Set plants under grow light 4.Count number of flowers

    11. GoodExample: 1. Fill 150 buckets with potting soil to within one inch of the top. 2. Plant one plant in each bucket so that all of its roots are covered with potting soil. 3. Water each plant with 1 liter of tap water. 4. Divide the plants into 3 equal groups of 50 plants each, groups A, B and C

    12. Step 4 - DATA Decide what DATA to collect – Gathering data accurately is one of the most important jobs of a scientist. Often things that seem unimportant at the time may later turn out to be quite significant. When designing an experiment, you must decide what and how you are going to collect the data. What will you measure. When will you measure it? How will you display the data once it is collected? Be sure to write it specifically in the procedures. *Typically the independent variable comes first in the data table followed by the dependent variable.

    13. Types of Data A. OBSERVATIONAL DATA: Often when experiments extend over a period of time,it is important to describe in words anything that seems significant. This may involve routine, daily or weekly descriptions in a journal format. Ex: Week 1 – No roses have flowered yet, not many leaveshave emerged yet Week 2 – I noticed that the roses in groups A and B are producing many more leaves that Group C plants are. Still no flowers on any plants.

    14. B. NUMERIC DATA: Numerical data must be displayed in a data table. The DATA TABLE should have a Title, and be carefully labeled. You must use a Straight Edge to draw your table

    15. Good vs. Bad Data Tables

    16. Graphing Data Each graph should have a title that describes what the graph is about When graphing data, the independent variable always goes on the X axis, and the dependent variable always goes on the Y axis. . Each axis of the graph should be marked off in equal units and properly labeled. In general, use a line graph when both axes are the set of real numbers. Ex. Time, temperature, height, weight. In general, use a bar graph when one or both of the axes are not the set of real numbers. Ex. Different kinds of roach spray, people.

    17. Step 5 - Conclusion -Restate your hypothesis. -State if your hypothesis was correct or incorrect. -Answer your problem in a complete sentence. (Always look back at your purpose (problem) to be sure you answer the right question). -PROVE your answer to your problem. Use ACTUAL DATA from your experiment to support your answer. This makes you more credible. -Restate your findings as a concluding thought.

    18. Conclusion SOURCES OF ERROR: Every time that you do an experiment, errors may occur. It is more important that you identify errors that you think could have affected the conclusion that you drew. Poor Example: I didn’t always measure accurately. Some plants may have gotten a little more water that the others. We had a lot of cloudy days which meant less light.   Good Example: It was discovered that in the green house, some of the plants were shaded for part of the day while others got full sun. Those that got full sun produced more flowers. Some of the plants became infested with tiny insects. These plants did not produce as many flowers as the others. Perhaps we should spray with an insecticide.  

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