Ms. Martin's IB Biology Page



Table of Contents TOC \o "1-3" TBLS lab report format and rubrics PAGEREF _Toc297252947 \h 2Biology lab report anchor paper PAGEREF _Toc297252948 \h 8Academic integrity PAGEREF _Toc297252949 \h 16Addressing measurement uncertainty PAGEREF _Toc297252950 \h 17Useful calculations PAGEREF _Toc297252951 \h 19Microsoft Excel quick reference PAGEREF _Toc297252952 \h 20Editing protocol for lab reports PAGEREF _Toc297252953 \h 23Data analysis editing checklist PAGEREF _Toc297252954 \h 24Evaluation editing checklist PAGEREF _Toc297252955 \h 25Final draft editing checklist PAGEREF _Toc297252956 \h 26TBLS lab report format and rubricsNameCourseTeacher NameDue DateTBLS labs contain these sections and each section heading is underlined and bold. The categories Design, Analysis and Evaluation do not need to be included.DESIGNResearch QuestionOne-sentence questionBackgroundParagraph giving scientific context and background related to the investigation and explaining personal interest in the topicHypothesisStated prediction with reasoningVariablesTable with the following column headers: Variable name, Variable type, and Method of control. Rows include IV, DV, and constants.MaterialsBulleted list of equipmentDiagramLabeled diagram of setupMethodNumbered list of stepsEthicsBulleted list of safety, ethical, and environmental issues related to the investigationANALYSISRaw DataRaw data, may also contain processed data if appropriateUncertaintiesBulleted list of measurement uncertainties with justifications for eachQualitative DataBrief paragraph of observationsSample CalculationsFormula and a worked example for each calculationProcessed DataTable and graphs presenting calculated valuesEVALUATIONConclusionParagraphs discussing the results and comparing them to previous research and accepted scientific knowledgeEvaluationParagraphs discussing sources of error and suggested improvementsDesign and Exploration Rubric Rubric Average: _______ / 4.00 100-scale: _______ = 70 + (Rubric Avg – 2.00) * 154321Formulating a focused research question -the purpose of the investigation is clear and narrowly focused on the study of a plausible cause and effect relationship-unambiguously identifies relevant independent and dependent variables with appropriate measurement words -the purpose of the investigation is plausible and focuses on a cause and effect relationship, but the scope of the investigation may not be fully focused- identifies relevant independent and dependent variables with appropriate measurement words-identifies a topic for scientific study, but the purpose of the investigation is unclear and/or- identifies independent and dependent variables of some relevancy but they may be poorly defined; measurement words are lacking where they are needed-the topic of the investigation is unclear or implausible or -fails to identify an independent or dependent variable, or identifies variables that are irrelevant to the proposed topic Contextualizing the investigation-fully and accurately explains all scientific concepts relevant to the investigation and uses them to formulate a plausible hypothesis -accurately explains most scientific concepts relevant to the investigation; may fail to ground hypothesis in these concepts-identifies some scientific concepts; their relevance to the experiment may be trivial, or they may be superficially or inaccurately explained-identifies no relevant scientific concepts; little effort is made to contextualize the investigationIdentifying variables-identifies all relevant constant variables unambiguously, using accurate measurement words whenever possible-identifies almost all of the relevant constants with measurement words; may omit or poorly phrase one or two-simplistic understanding of constant variables; may omit many or include variables that are outside of the control of the experimenter (e.g. air pressure); a number of variables may lack measurement words-fails to control variables necessary for making a meaningful conclusion Materials and Diagram-selection of tools is fully appropriate to the investigation and all equipment is clearly identified by its name, size, and uncertainty-includes a clear, fully labeled diagram that clarifies the most important steps of the procedure-selection of tools is generally appropriate and most are clearly identified, with some gaps or inconsistencies -includes a diagram that clarifies some steps of the method; it may be incompletely labeled -selection of tools is consistently inappropriate, or it is not possible to determine appropriateness because tools are insufficiently identified-diagram may be missing, unclear, or inconsistent with the procedure; it may depict trivial steps that don’t require further clarification-report shows little understanding of the equipment involved in the investigation; scientific tools are rarely mentioned, or tools are used incorrectlyMethodology-method is specific enough to replicate exactly; each procedure step flows from the previous one and includes the measurements timing, and materials involved- repetition steps are included when needed - method is usually specific; some measurements, timing, and materials may not be specified when they need to be- repetition steps may be missing or unclearly phrased, so it is not clear how to collect all raw data-method allows for the collection of data, but significant gaps or vagaries make it difficult to replicate-there may be inconsistencies within the method and table of variables that call into question the quality of the data and the control of constants-method is vague or overly general; steps for measuring the independent and dependent variables are unclear or omitted014605Considering safety, ethics, and the environment: The report shows full awareness of the safety, ethical, and environmental issues involved in the investigation. If there is no mention, or if the report only acknowledges the need for awareness without identifying specific issues relevant to the investigation, deduct 2 points from the design rubric total before averaging.00Considering safety, ethics, and the environment: The report shows full awareness of the safety, ethical, and environmental issues involved in the investigation. If there is no mention, or if the report only acknowledges the need for awareness without identifying specific issues relevant to the investigation, deduct 2 points from the design rubric total before averaging.Analysis Rubric 4321Raw Data Quality and Quantity-report includes raw data that spans an appropriate range of independent variable conditions and has a sufficient number of trials-quantitative data is supported by detailed and relevant qualitative observations-report includes raw data that spans an appropriate range of conditions, and a sufficient number of trials are planned, but data collection may be incomplete-some qualitative data but it may be shallow or inappropriate -some relevant data is collected but it may not span an appropriate range of conditions, may not include repeated trials, or may be missing qualitative data; as a result, the data will support only a very simple conclusion-report contains insufficient relevant data; no valid conclusions can be reached that relate to the research questionCalculations-clear sample calculations, including formulae and worked examples, demonstrate that all processing has been done accurately-calculations usually include formulae and a worked example, and the processing is generally accurate, but there are minor errors or omissions -sample calculations are consistently missing either a formula or a worked example or-processing contains significant inaccuracies - sample calculations are missing or too unclear to verify the accuracy of the work or-processing is too inaccurate for a valid conclusion to be drawnData Processing and Graphing-chooses calculations that analyze both average and variance -presents the results in an appropriate graph; if appropriate, the graph has a justifiable trend line -if needed, error bars correctly show either the variance or measurement uncertainty, and the trend line passes through all error bars-chooses calculations that analyze both average and variance-presents the results in an appropriate graph; there may be a trend line with some basis but it attempts to over-fit the data- error bars are needed but they may be missing, poorly chosen, or plotted incorrectly -some basic data processing is carried out, but it is simplistic-presents results in a graph that shows a relationship between the IV and DV, but the type of graph may be inappropriate for the data set; it may include a trend line with no basis in the data, or a trend line is missing but needed-data processing neglects a critical calculation or inappropriate calculations limit ability to draw a conclusion or-graphing is too inappropriate or limited for a valid conclusion to be drawn from the data (i.e., there is no graph at all, or the graph does not allow analysis of the relationship between IV and DV)Uncertainty-accurately describes the uncertainties of all measurements, records data to a place value that agrees, and maintains a consistent uncertainty throughout-if necessary, propagates uncertainty fully and accurately -identifies the uncertainties involved in measurements, but may lack nuanced understanding; there may be inconsistencies in the way data has been recorded-propagates uncertainty when it is necessary, but there are errors-shows a simplistic understanding of uncertainty; a few uncertainties are missing where they are needed or included where they are inappropriate; recording of raw or processed data may consistently ignore uncertainty-no measurement uncertainties are reported, or-most or all uncertainties are incorrect or inappropriate DataPresentation-data are presented in well organized tables that are formatted with clear borders, even spacing-headings are easy to read; a line break separates variables from the unit and uncertainty-graphs have an appropriate size and scale -data are presented in tables that are generally well organized and formatted with legible headings-graphs have an appropriate size and scale-there are some minor formatting inconsistencies-raw and processed data are presented in tables, but they may be poorly organized; some data may be repeated or out of place -graph may have a confusing or inappropriate scale -awkward formatting or page breaks hinder interpretation -raw or processed are not consistently presented in tables, or-tables are so poorly organized or formatted that it is unclear what data has been collected or how it was processedEvaluation Rubric43 2 1Concluding based on data-fully and accurately summarizes trends in the data (or the lack of trends); if possible, correctly determines a mathematical relation between IV & DV-uses data to construct a logical argument that answers the RQ-evaluates the reliability and scope of the conclusion based on the quantity and quality of data collected, and revises the conclusion appropriately -accurately summarizes trends in the data, lacking specificity; may miss nuances or fail to determine a mathematical relationship when it is possible-data is used to address the research question but the logic may have some inconsistencies-comments on the reliability or scope based on the available data, but may not revise the conclusion accordingly -conclusion offers an answer to the research question that could be supported by the data presented, but references to the data are unclear or missing-the argument may have many logical leaps or inconsistencies-may make a general comment about the strength of the conclusion, but fails to reference specific data points or aspects of the conclusion-conclusion does not answer the research question or -offers an answer that cannot be substantiated by the data presented or-the conclusion is very simple and little or no attempt has been made to justify it with argument or dataContextualizing the conclusion-uses accepted scientific theory to explain trends in data and, if necessary, explains how data differs from accepted theory -if appropriate, determines or finds an accepted value and accurately calculates the % error of experimental values -uses accepted scientific theory to explain trends in data, but explanation is incomplete-may attempt to calculate % error, but with some mistakes-may refer to a relevant scientific theory, but does not clearly explain how it relates to the data collected-does not reference an accepted value when it is appropriate to do so-makes no references to relevant scientific theories when it would be appropriate to do so, or inaccurately describes science concepts related to the conclusionIdentifying limitations-identifies sufficient relevant limitations resulting from the experimental design and methodology -identifies some relevant limitations that result from the experimental design, but one limitation may be trivial; may overlook a notable weakness-identifies some relevant limitations, but they generally result from the practical or procedural issues faced in the lab rather than design flaws-does not identify limitations or identifies only limitations that are trivial or overly vague (e.g. “human error”)Evaluating limitations-fully describes how all identified limitations skew the data higher or lower or increase random error-describes how most identified limitations skew the data or increase random error, but shows incomplete awareness of how one has impacted data-suggests that limitations have affected the quality of the data, but consistently fails to explain how the data has changed as a result of these limitations-shows little awareness that limitations may affect dataSuggesting improvements-fully describes specific, realistic procedural revisions and explains how they would reduce the errors identified previously -describes some realistic procedural revisions, but a few suggestions lack specificity or do not clearly reduce errors that have been identified-very few realistic revisions are offered; most suggestions are only superficially described or do not reduce errors that have been identified -offers no suggestions for improvements or all suggestions are superficial, irrelevant, or unrealisticCommunication Rubric43 2 1 Scientific ConventionsReport observes all of the following consistently:-tables have titles that provide relevant context -column headers include variable and units-axis labels include variables and units-graphs include a title and, if necessary, a legend -graphs and images are labeled “figure #:”, tables are labeled “table #:”, and there is a consistent numbering system Report generally observes all of the conventions in [4], but there are a few minor inconsistencies Only three or four of the conventions in [4] are followed with any consistencyOne or none of the conventions in [4] are followed consistentlyFormatting and Organization -tightly follows TBLS lab report format and all major sections are included and clearly labeled with bolded headings-page breaks are included whenever needed to prevent awkward disruptions in tables or headings-uses a consistent font and size - follows the general outline of the TBLS lab report format and all sections are included, but some headings or labels may be unclear-there may be some awkward page breaks or font usage but the outline of the report is clear -vaguely follows TBLS lab report format, but section headings are missing or some sections are missing-poor formatting may distract the reader from the content of the work-the organization and division of the work into sections is incoherentClarity -report is relevant and concise, facilitating a thorough understanding of the topic-consistently and correctly uses scientific terminology -no or very few typos-report includes some irrelevant or repetitive material that neither helps nor hinders understanding-consistently uses scientific terminology, but may uses some terms imprecisely -there may be a few noticeable typos-parts of the report contain irrelevant material that hinders understanding -often uses terminology inaccurately or relies on colloquial language-report may be riddled with typos, but it remains comprehensible-much of the report is unclear as a result of imprecise language or the presence of irrelevant or inappropriate material Biology lab report anchor paperDiscipula MaximaMagister EinsteinIB Biology HL31 December 1999Research QuestionHow does temperature affect the movement speed of Armadillidium vulgare?BackgroundInnate behaviors have evolved to help organisms survive in their environment. An innate behavior shown by many invertebrates is kinesis, an increase or decrease in random movement in response to a stimulus. Typically, organisms increase their speed of movement when in unfavorable conditions and slow down their movement in favorable conditions. This pattern allows them to spend a greater time in favorable conditions, which is helpful for survival. Armadillidium vulgare, also known as the pill bug, is a terrestrial invertebrate that is known to demonstrate kinesis in response to humidity. Pill bugs are vulnerable to dehydration in dry climates. The kinesis behavior allows them to locate moist environments and avoid water loss. The purpose of this experiment is to determine if pill bugs have a kinesis in response to temperature changes in their environment. Pill bugs are ectotherms, so their internal temperature depends on the surrounding environment. Temperature extremes are harmful, so it is plausible pill bugs will have evolved an innate behavior to sense and respond to temperature changes. HypothesisAn increase in environmental temperature will decrease the speed of the pill bugs, because as ectotherms they will prefer warmer temperatures to avoid freezing.Table of VariablesVariableType Method of ControlTemperature (°C)IndependentThree temperatures are tested: 15°C, 25°C, and 35°C. The pill bug will be placed in a petri dish. The temperature will be changed by placing the petri dish in either an ice bath, a warm water bath, or on the table at room temperature. The temperature within the petri dish will be measured using a standard thermometer (+/-0.5°C). Movement speed (cm/s)DependentThe floor of each petri dish will be lined with graph paper with 1cm x 1cm grid boxes. The pill bug’s motion will be recorded with the camera of an iPhone for 2.5 minutes. When the video is played back, the experimenter will count the number of grid boxes the pill bug crosses during 30 second intervals. The speed of each interval will be calculated: Speed=# of boxes30sVariableType Method of ControlDish sizeConstantAll petri dishes are 10 cm in diameter and 0.5 cm tall.Dimensions of grid squareConstantThe squares of the graph paper are 1cm by 1cm for each trial and condition, allowing an estimate of the distance traveled.Equilibration timeConstantBefore starting data recording, the pill bug will be placed into the environment and allowed 1 minute of rest time to equilibrate to the environment and overcome the shock of being moved by the experimenter.Trial timeConstantData will be recorded over 2.5 minutes (150s), in 30s intervals.Rest time between trialsConstantEach pill bug is tested at all three temperatures, with at least a 3-minute rest between trials in a petri dish at room temperature.Light levelConstantAll trials will be conducted in the TBLS lab at a lab bench far from the window, with the overhead lights off. Camera distance and positionConstantThe iPhone camera lens is positioned directly over the center of the petri dish, held in place by a ring stand at a height of 15 cm above the petri dish.MaterialsPill bugs (5)10-cm petri dishes (2)Paper with 1cm2 gridlines, cut in a 10-cm diameter circleRuler (+/- 0.05 cm) iPhone with video cameraRing stand and ring clampHot Plate1000-mL beakerCrushed ice or fresh snowWeigh boat (1)Thermometer (+/- 0.5°C)Wooden coffee stirrerDiagramFigure 1: Setup of petri dish for all conditions (ringstand not shown)Figure 2: Setup for 15°C condition:2057400137795weigh boat with ice00weigh boat with ice1143001587500Figure 3: Setup for 25°C condition:0000MethodPreparation of the petri dishes (see diagram 1)Data recording chamber: Trace the petri dish on the graph paper and cut out the traced circle with scissors. Place the graph paper in one petri dish.Rest chamber: Trace the petri dish on a paper towel and cut out the traced circle. Place the paper towel in the chamber and moisten with tap water dropped from a pipette, to create a humid environment that is safe for the pill bugs. Add just enough water to moisten the paper towel. Too much water can drown the pill bugs.Collect five pill bugs from the class tank and transfer them, one at a time, to the rest chamber. Place the lid on the chamber.For room temperaturePlace the experimental chamber on the lab bench. Place a thermometer at the base of the chamber. It should be at room temperature (20-25°C). Record the actual temperature.Set up a ring stand with a ring clamp 15 cm above the center of the dish. Place the iPhone on top of the ring clamp with the camera lens facing down.Transfer one pill bug to the petri dish carefully using a wooden coffee stirrer. Wait one minute for the pill bug to equilibrate to the new environment.Start the iPhone recording. Observe the behavior of the pill bugs.Stop the recording after 2.5 minutes. Transfer the pill bug to the rest chamber.Repeat steps 6-9 using a new pill bug each time until all five pill bugs have been tested.For ice bath (see diagram 2)Fill a large weigh boat with crushed ice or fresh snow.Place the experimental petri dish on the ice-filled weigh boat. Nestle it into the ice.Position and adjust the ring stand so the camera is 15 cm above the floor of the dish.Place the thermometer on the floor of the petri dish and monitor the temperature.Once the temperature is 15°C, transfer the pill bug to the petri dish carefully. Repeat steps 7-10 until all 5 pill bugs have been tested in this temperature.Between each trial, place the thermometer on the floor of the petri dish. If the temperature is too low, remove the petri dish from the ice temporarily until temperature is 15°C again, then resume data collection.For hot water bath (see diagram 3)Fill the 1000-mL beaker with 600 mL of water and place it on a hot plate.Place the prepared petri dish into the beaker and make it float on the water.Insert a thermometer into the water. Adjust the ring stand so that the camera is positioned 15 cm above the petri dish. When the temperature reaches 35°C, place 1 pill bug in the petri dish.Repeat steps 7-10 until all 5 pill bugs have been tested in this temperature.Monitor the temperature throughout trials. If the water bath temperature gets too high, stop taking data, turn off the hot plate and add ice to cool until it has reached 35°C again.Analyzing dataView each video at low playback speed and count the number of boxes crossed by the pill bug during each 30 second interval. Safety and EthicsLimit stress to pill bugs. Wash and rinse hands thoroughly before handling pill bugs, and always handle them gently, one at a time. When pill bugs are not in use, they should be in the rest chamber with a moist paper towel. If pill bugs show distress, stop the trial immediately. Raw DataTable 1: The effect of temperature on distance traveled by the pill bugs as recorded in 30-second intervalsTemperature of the environment°C ± 1 °CDistance traveled per 30s interval cm ± 0.5 cm0-30s30-60s60-90s90-120s120-150s1510.012.08.08.06.06.08.09.05.08.08.06.07.010.06.010.010.08.06.06.08.05.08.07.08.02215.013.010.014.013.014.016.016.012.014.013.011.014.012.015.016.013.015.014.011.015.013.015.014.012.03520.017.016.018.018.015.018.016.019.018.020.018.018.019.020.018.020.019.017.015.018.020.019.020.017.0UncertaintiesTemperature (+/- 1°C): An analog thermometer was used with increments of 1°C. The measurement uncertainty is +/-0.5°C. However, I continued recording data as long as the temperature was within 1°C of the desired temperature, so I chose to use a higher uncertainty value. Distance (+/- 0.5 cm): Each grid box is 1cm in length. This works like an analog instrument, so the measurement uncertainty is half the smallest increment.Time (+/- 0.01s): The digital clock on the iPhone measures to the hundredth of a second, and I can be this precise because I slowed down the video playback to get an accurate count.Qualitative DataWhen transferred into the container, the pill bugs rolled into a ball for a short time. When they unrolled, they moved mostly in a circular fashion around the perimeter of the dish. They will often stop and turn their fronts toward the petri dish wall, probing it with their antennae. Their movement patterns, independent of speed, were similar at all temperatures.Sample Calculations:First I calculated the average speed per 30-second interval:Speed= distancetimeSpeed for 15°C Trial 1= 10 cm30s= 0.33 cm/sThen I calculated the average speed over the 2.5 minute trial for each pill bug:Average= sum of all values# of valuesAverage for 15°C Trial 1= 0.33+0.40+0.27+0.27+0.20 (cm/s)5=0.29 cm/sI averaged the average trial speeds for all of the pill bugs at the same temperature:Average= sum of all values# of valuesAverage for 15°C = 0.29+0.24+0.25+0.27+0.24 (cm/s)5=0.26 cm/sFinally, to analyze variance amongst the pill bugs, I found the standard deviation of the average trial speeds at the same temperature using the =STDEV.S function in Microsoft Excel:=STDEV.S (0.29, 0.24, 0.25, 0.27, 0.24)= 0.02 cm/sI then plotted a scatterplot graph to show the relationship between the temperature of the chamber and the average speed of the pill bugs. The trend appeared approximately linear so I plotted a linear line of best fit.Processed DataTable 2: Calculating the average speed of pill bugs at different temperaturesTemperature of the environment °C ± 1°CSpeed of the pill bugs at 30 intervalscm/sAverage speed per pill bugcm/sAverage speed of pill bugs in each temperaturecm/sStandard Deviation0-30s30-60s60-90s90-120s120-150s150.330.400.270.270.200.290.260.020.200.270.300.170.270.240.270.200.230.330.200.250.330.330.270.200.200.270.270.170.270.230.270.24220.500.430.330.470.430.430.450.020.470.530.530.400.470.480.430.370.470.400.500.430.530.430.500.470.370.460.500.430.500.470.400.46350.670.570.530.600.600.590.600.030.500.600.530.630.600.570.670.600.600.630.670.630.600.670.630.570.500.590.600.670.630.670.570.63Conclusion:The purpose of this investigation was to determine if Armadillidium vulgare shows kinesis in response to temperature changes. The data suggests they do. There is a positive correlation between temperature and the speed of pill bug movement, as shown by the trend line in the graph. Thus, pill bugs move more quickly in warmer temperatures and more slowly in colder temperatures. This contradicts my initial hypothesis. There was very little variation between pill bugs, as shown by the small standard deviations. All pill bugs responded very similarly at each temperature, which supports the idea that this behavior is genetically inherited.The trend line shows a linear relationship, but one point (22°C) does not fit this pattern (the trend line does not pass through the error bars of this point.) The change in speed was greater from 15°C to 22°C than from 22°C to 35°C, even though the temperature charge was not as large. This could mean that the relationship is not linear, and that the pill bugs have a maximum speed at which the line plateaus. This idea is speculative, as my experiment only tested a limited range of temperatures. My experiment also does not allow for conclusions about the behavior of pill bugs in extreme cold.The results of this experiment are consistent with other research that has shown that pill bugs have a kinesis response to temperature. Pill bugs are vulnerable to desiccation, and higher temperatures cause faster evaporation of water. Thus, it has been suggested that pill bugs increase their rate of movement in warm conditions in order to boost the odds of reaching a more favorable environment. Our experiment supports this theory because pill bugs exhibited an increase in kinesis as temperature increased, and the behavior was the same in all of the pill bugs.Evaluation:It was difficult to maintain the temperature of the petri dish. The cold chamber continued to cool throughout the trials, leading to lower speeds. On the other hand, the hot water bath continued to heat up, leading to higher speeds. It can still be said for sure that the cold trials and hot trials are relatively colder and hotter than room temperature, but the exact temperatures are not known precisely. This could be improved by conducting the trials in an incubator set at a stable temperature. The camera could be placed in the incubator to record the trial. This would reduce the uncertainty of the temperature measurements.Another limitation was that there were only three temperature conditions tested. As discussed previously, this limits the conclusions that can be drawn. This could be improved by repeating the investigation at other temperatures between 10°C and 35°C to better define the trend line. If there were data points for 10, 20, 25, and 30°C, the trend line would be clearer and it would be possible to know if the relationship was truly linear or not.A final limitation was that the distance traveled was not measured directly. This distance was approximated by counting the number of grid boxes crossed by the pill bug. Because the petri dishes are circular, many boxes are incomplete at the edges. It takes less time to cross these, but it still counts as crossing 1 full box, equal to 1 cm. As a result, this method overestimates the distance actually traveled. The pill bugs spent most of the time moving circularly around the edges, so this error is probably significant. This could be avoided in the future by using a square enclosure that just fits a 10 x 10 grid, so that no boxes are cut off at the edges. Every box crossed would consistently be a 1 cm distance.Academic integrityAs in all disciplines, an authentic piece of work in science is one that is based on your individual and original ideas. Where the ideas or work of another person are represented within your work—whether in the form of direct quotation, paraphrase, or diagram—the sources must be fully and appropriately acknowledged.The integrity and honest representation of data is paramount in science—results should not be fixed or manipulated or doctored. Data is the lifeblood of scientists and may be obtained from pure observation or from specifically designed experiments. In all cases, data presented in a lab write-up is intended to report what has taken place in the real world. If a student manufactures or manipulates data for a table, survey or other such requirement, it is scientific and academic fraud. The following declaration must appear in your final work:“I do hereby affirm that this work is my own original composition. I have written this report myself in order to represent the ideas and thinking of my group. Except for the sources I have acknowledged within, all ideas were developed by my group members and myself. Unless otherwise stated, all data and observations were obtained in the TBLS lab using the procedures described herein. I affirm that all data are authentic, and I have neither changed nor invented data in the process of reporting and analyzing my findings.”CitationsCitations commonly come up in the following sections of the lab report:Background: To refer to previous research or scientific content related to your topic, you must cite the research. Remember, your textbook also counts as a source!Method: You must cite the original source of your measurement protocol! This applies whether you used this lab manual or did your own web research.Diagram: If you take an image from the Internet, you must cite the source.Calculations: If you are calculating percent error by comparing your result to a standard value, you must cite the source of the standard value.Conclusion: When comparing your results to previous research and established knowledge, you must cite the sources you used. If applicable, include your textbook.You may cite either using footnotes or by making parenthetical references and including a Works Cited as the final section of your lab. Either way, citations should be in MLA format. Refer to the Online Writing Lab at Purdue: measurement uncertaintyRandom error in an experiment generally arises from the limited accuracy of the measuring instruments. An uncertainty range applies to any experimental value. Some instruments, like a digital scale, state the degree of uncertainty. Others, such as rulers or graduated cylinders, do not. In those cases you have to make a judgment regarding the degree of uncertainty.Examples of InstrumentsRule for Uncertainty ValuesAnalogueGraduated cylinder, thermometer, ruler, etc.The uncertainty value is ± half the smallest division.DigitalDigital scale, temperature probe, etc.The uncertainty of a digital scale is ± the smallest scale division. Fig. 1Fig. 2Fig. 3Fig. 1 represents an alcohol thermometer with the smallest scale division of 1°C. The uncertainty value is 0.5°C, so the temperature should be recorded as 23.5 °C ± 0.5°C. The last digit in bold represents an estimated value, since this digit of the thermometer reading falls in between the smallest scale divisions. This digit represents the reported uncertainty value.Fig. 2 represents a volume reading taken from the bottom of the meniscus. The smallest scale division is 2 cm3, so the volume should be recorded as 38 ± 1 cm3. The last digit in bold represents an estimated value, since this digit falls in between the smallest increments. This digit represents the reported uncertainty value.Fig. 3 represents a digital scale. The mass is recorded as 329.43 ± 0.01 g. The digit of uncertainty (in bold) is already implied within the reported value given by the instrument.Place value in measurementsThe digits in the measurement up to and including the first uncertain digit are the significant figures of the measurement. These should correspond to the uncertainty values, and all data should be recorded to the same decimal place.Example: CORRECT INCORRECT?Initial Massg ± 0.01 g?Initial Massg ± 0.01 gTrial 1101.43Trial 1101.499.8999.89102.56102.6101.00101.00100.00100.0Other sources of uncertaintyUncertainties in judgment, such as color change from an indicator, can be difficult to quantify. These types of uncertainties should be noted even if they are not actually quantified.Uncertainty in calculationsWhen performing a calculation, the answer should be rounded to the same number of decimal places as the least precise measurement.Examples:A student records the mass of a solution of sodium chloride to be 5.00 ± 0.01 g, and the volume as 2.3 ± 0.1 cm3. What is its density?Density= massvolume= 5.00 g2.3 cm3=2.173913043 g cm-3The uncertainty of the density calculation is determined by the volume measurement, since it is less precise than the mass measurement. The second significant figure is therefore the uncertain digit, and the reported value must be recorded as 2.2 g cm-3.A student is asked to report the concentration of a solution prepared by adding 1.00 g of sugar to 50 mL of water.Concentration= mass solutevolume solution= 1.00 g50 ml=51 g ml-1The uncertainty of the concentration measurement is +/- 1 g ml-1. This value corresponds to the least precise measurement, which is based on the uncertainty of the graduated cylinder.Useful calculationsMeanSum of all valuesNumber of valuesUsed to find a single number that represents the data set.MedianThe middle number when data are arranged sequentiallySame use as mean, but is best for data sets with a significant outlier that would skew the mean.Half RangeMax value-Min value2Used to find a number that represents the spread of the data. Half range can be used to plot error bars. Standard DeviationEasiest to do on a graphing calculatoror in Excel as =STDEV.SSame use as half range, but is appropriate for normally distributed data (like many biological traits).Percent ChangeFinal-InitialInitial x 100%To show how much a variable has changed; useful when the initial values are very different. 0% indicates there has been no change.Percent of Initial ValueFinalInitial x 100%Same use as percent change, but here 100% means no change.Percent of ControlExperimental value-Control valueControl value x 100%To compare experimental values to a control group value.Percent ErrorActual-ExpectedExpected x 100%To compare an experimental result to a known standard value.Inverse Time1TimeTo transform time measurements into rates, so that the largest value indicates the fastest rate.Rate? Variable? TimeTo compare how fast different reactions or processes are happening. Concentration (molarity)Moles of SoluteVolume of Solution (L)Standard measure of concentration. Whenever possible, this is the measure of concentration that should be used.Concentration(% by volume)Volume of SoluteVolume of Solution x 100%Measure of concentration when the solute and solvent are both liquid. Concentration(g / L)Mass of solute (g)Volume of solution (L)Measure of concentration when the solute is a powdered solid, and it is not practical to find the molarity.Microsoft Excel quick referenceBasics of navigating ExcelYou should notice that each cell has its own “address” comprised of its column letter and its row number (ex: A1, G4, H10, etc.) These addresses come in handy when performing calculations.When using Microsoft Excel, it is very important to pay attention to where your cursor is and to what you have selected. Your cursor can take on different shapes, and each shape has its own function. A few common cursors in Excel:White CrossCross with Arrows480060068580Drag: moves entire selected area0Drag: moves entire selected area32004006858000182880068580Cursor for selecting cells0Cursor for selecting cells4572006858000Double Headed ArrowBlack Cross502920038100Select the corner of a cell and drag and it copies whatever is in that cell00Select the corner of a cell and drag and it copies whatever is in that cell182880038100Drag: Resizes column or row00Drag: Resizes column or row320040038100004572003810000Formatting your tableResize a column or row – Place mouse over the line that separates the two columns or rows in the reference row (capital letters for columns, numbers for rows). A special cursor will appear. Click and drag the line to the left or right.Insert column – Highlight the column to the right of where you would like to insert. Right-click on the letter at the top then select insert. Insert row – Highlight the column below where you would like to insert. Right-click on number at the left edge and select insert.125730028956000Merge cells – Highlight the cells you wish to merge. Go to the Home tab and find the “Merge & Center” button: Create borders: There are many different options for creating borders. They can all be found in the border drop down menu in the Home tab. Either highlight the cells you would like to place borders around, or select “Draw Border” and draw them in yourself.13716001587500Formatting cells1905040322500Wrapping text inside a cell – Select the cell in which you would like the text to appear on multiple lines and then press the “Wrap Text” button: Creating a new line inside a cell – Hold down the Shift key while pressing Enter.Changing the format that displays in the cell (decimal, scientific notation, date) – Select the cell (or cells) whose format you would like to change. Right click on the cell and then select “Format Cells” in the menu that appears. Under the Number tab, chose the format display you would like to use from the Category list and then hit OK. Changing the number of decimal places displayed – Follow the instructions above for changing the format. When you get to the Category list, select Number, then adjust the number next to decimal places. You can also do this more quickly by selecting the cells on your spreadsheet and using the decimal place adjuster hot button in the editing menu. The left arrow decreases the number of decimal places and the right number increases them, rounding appropriately along the way.2057400724535 Working with equationsSelect the cell that you wish to place an equation inType an = and then the function or equation that you would like Excel to calculate* for multiply/ for divide+ and – for add and subtractAverage(list of cells) to averageTo reference a cell, simply click on that cell. The cell will appear by its column and row designation, e.g. H7To reference more than one cell, either click and drag if they are next to each other, or hold down the CTRL key as you select the cells. The list of cells will appear separated by commas or first and last cell in the list separated by a colon.Example: =AVERAGE(E9,F9,G9,H9,I9) or =AVERAGE(E9:I9)Creating and manipulating graphsSelect the cells that contain your independent variable data, then hold down the Ctrl key and select the cells that contain your dependent variable data averages.Once all cells are selected, go to the Insert tab, and click on the type of graph you want to add (usually this will be a scatterplot with only markers).Once your graph appears, use the tabs that appear under Chart Tools (Design, Layout and Format), to manipulate your graph. You can add a title and axis labels, as well as change the color and shape of your markers.Adding a best-fit line – Under the Layout tab under Chart Tools, click on the Trendline drop-down menu, then select the type of best-fit line you would like to add. If none of the lines are a good fit for your data, you may try to use the “Smooth Marked Scatter” option to add a smooth line that connects all your points. If your data fluctuates up and down with no seeming pattern, or if your error bars are large and overlapping, it may be a sign that there is no relationship in your data. In that case, you should leave the trend line off.Adding error bars – In a new column, enter the measure of variance for each of your data points. Go to the Layout tab under Chart Tools and select the Error Bars drop down menu. Select “More Error Bar Options.” Select the option at the bottom that says Custom, then click the “Specify Value” button. Once the new window pops up, click on each of the two boxes and select the column containing your uncertainties. Then hit “OK” and then close.Editing protocol for lab reportsInstructions: Read through a partner’s draft with a pen in hand. Consider the following and mark any issues you see. Write the feedback directly on the person’s draft. Organization: Does the lab report contain all nine sections (Research Question, Background, Hypothesis, Variables, Materials, Diagram, Method, Safety and Ethics, Works Cited)? Is the start of each section easy to find and clearly indicated with bolded headings? Are there awkward page breaks or formatting?Citations: Does the lab report use parenthetical references to cite the use of ideas from other sources and references that are presented in the lab? Look in the background paragraph and method. The class packet should be cited.Measurement words: Does the lab report consistently use measurement words (mass, volume, length, etc – never “amount”) to name dependent, independent, and constant variables? Are the units of measurement included whenever necessary? Is it clear how to measure each variable in the lab?Equipment: Whenever a verb like “measure”, “cut”, “transfer,” or “stir”, etc. appears, does the lab report specify what tools to use? Is all equipment fully identified by its correct name, size, and uncertainty? (Ex: 100-ml beaker or digital scale (+/- 0.1g)) Repetition: Is it clear which steps should be repeated, when they should be repeated, and how many times, in order to collect data for all trials? Is it clear how many data points are being collected in total?Robot test: If a robot were following the instructions of the lab and could only do exactly what is written, what steps would the robot get tripped up on? How could these steps be broken down or described more clearly and specifically?Consistency: Are the materials, procedure, table of variables, and diagram 100% consistent with one another? For each constant, are there method steps that describe how to maintain that constant? Does the method refer to all of the equipment in the materials list? Are there any inconsistencies at all?Data analysis editing checklistLook closely at the raw data table:All of the columns have a unit in the column header.All of the columns with measurements have an uncertainty value listed. Final Massg ± 0.1 gThe unit and uncertainty are centered beneath the variable (see text box).All data in a given column is recorded to the same number of decimal places, and the number of decimal places matches the uncertainty.Each column is about the same width and each row is about the same height.The table fits within the page margins. Data within the table is recorded as numbers only (no units).A title appears above the table that describes the data and the context of the experiment.Look closely at uncertainties:Uncertainties are listed and justified using the uncertainty rules we learned.It is clear how each of the uncertainties listed relates to the raw data table. For example, for a concentration variable, the uncertainty of a graduated cylinder and a scale should be listed.Look closely at the qualitative data:The qualitative data is in the form of a descriptive paragraph.The qualitative data has NO commentary on the numbers (e.g. “the mass increased”).The qualitative data is useful and relevant to the experiment.Look closely at the processed data table:Processed data table contains columns for the IV, average DV, and a measure of variance (half range or standard deviation) for each average.All of the columns have a unit in the column header.The unit is centered beneath the variable (see above).Each column is about the same width and each row is about the same height.The table fits within the page margins.Data in the table is recorded as numbers only (no units).A title appears above the table that is different than the title for the raw data table.Look closely at the sample calculations:Sample calculations are present for all calculated values in the processed data.Every sample calculation includes the formula. Every sample calculation includes one worked example.The worked examples consistently include the units of measurement.The calculations look neat and were completed using the Equation Editor in MS Word.Look closely at the graph:Both axes are labeled with the correct variable and unit.The graph has a descriptive title that refers to both axes and identifies the trend or lack thereof.The graph takes up at least half a page.Vertical error bars are present, and there are no horizontal error bars.A sentence near the graph (or in the title) clearly states what the error bars represent. The error bars are plotted correctly and match the values in the processed data table.Unless there are many data points or the x-axis is time, the points are not connected by a line.There is a trend line (line of best fit) that seems to follow the pattern of the plotted points and passes through all the error bars.Evaluation editing checklistLook closely at the conclusion:The conclusion completely describes the patterns revealed by the trend line in the graph. If there is no trend, this is clearly stated in the conclusion.The conclusion is specific and includes minimum and maximum values, the range of tolerable concentrations, etc. as appropriate.The conclusion explains what the patterns in the data reveal about the research question.The conclusion compares the findings of this investigation to what is already known about the topic, identifying whether the investigation confirms, refutes, or expands current thinking.The conclusion cites sources as necessary.Look closely at the evaluation:There are at least three paragraphs, each one addressing a different limitation.No more than one of the limitations is a practical or procedural error (such as a mistake that was made or a lack of sufficient time).Limitation 1The limitation is identified as a systematic or random error (if appropriate).There is a clear explanation of how the limitation affected the data that was collected. If the limitation is a systematic error, the report clearly identifies whether the error skewed the data up or down.There is a suggested improvement that would realistically improve the limitation in future work.The suggestion is specific, including equipment that would be needed and measurement values. The suggestion reads like a procedure step.Limitation 2The limitation is identified as a systematic or random error (if appropriate).There is a clear explanation of how the limitation affected the data that was collected. If the limitation is a systematic error, the report clearly identifies whether the error skewed the data up or down.There is a suggested improvement that would realistically improve the limitation in future work.The suggestion is specific, including equipment that would be needed and measurement values. The suggestion reads like a procedure step.Limitation 3The limitation is identified as a systematic or random error (if appropriate).There is a clear explanation of how the limitation affected the data that was collected. If the limitation is a systematic error, the report clearly identifies whether the error skewed the data up or down.There is a suggested improvement that would realistically improve the limitation in future work.The suggestion is specific, including equipment that would be needed and measurement values. The suggestion reads like a procedure step.Final draft editing checklist Look at the entire document. Consider conventions, formatting, organization, and clarity.All of the required sections of the TBLS lab report format appear in the correct order, and each section has the correct format.Report begins with a proper MLA heading. Report is NOT double-spaced.The report contains all sections with clear, bolded, and underlined section headings.There are no awkward page breaks (page breaks that interrupt tables, separate titles from the table or graph they go with, or separate a section heading from the section it goes with).The report uses a consistent font style and size.There are no typos.Every table and graph has a title.Table titles are preceded by “Table #:” and a consistent numbering system is used.Titles for graphs and diagrams are preceded by “Figure #:” and a consistent numbering system is used.Conclusion refers to data by table and figure # (e.g. “As seen in Figure 1…”) when appropriate.Redundant or contradictory information has been removed—report is concise and consistent. ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download