IB Biology Lab Tool-Kit

Transcription

1 IB Bilgy Lab Tl-Kit There are tw main types f investigatins that yu will perfrm in IB bilgy: 1. Experiments are studies that allw scientists t manipulate a variable and bserve its effects. Fr example: Des changing light affect the grwth f radishes? Experiments are pwerful studies because they can establish whether a variable influences r determines an utcme. 2. Smetimes experiments are neither pssible nr desirable. Human subjects, fr example, are ften unsuitable fr experimentatin fr ethical reasns. Jane Gdall, wishing t discver the behavir and scial structure f chimpanzees in their natural habitat, did nt perfrm experiments with her subjects but instead bserved them with minimal human interference. When subjects are studied as is rather than manipulated in cntrlled settings, they are part f descriptive studies. The purpse f writing a lab reprt is t determine hw well yu perfrmed yur investigatin, hw much yu understd what happened during the prcess, and hw well yu can cnvey that infrmatin in an rganized fashin. Remember that lab reprts are individual assignments. Yu may have had a lab partner, but the wrk that yu d and reprt n shuld be yur wn. LOGISTICS: Raw data must be cllected in a bund lab bk Lab must be typed r neatly hand written Title f lab is clear and relevant 1.5 line spacing Lgical rder, with clear headings The spelling, grammar, and flw f the writing must be understandable. When yu write a lab reprt, yu will have already perfrmed the investigatin. Please use the past tense thrughut the paper. DESIGN ASPECT 1: Defining the Prblem and Selecting Variables Include a Backgrund Infrmatin sectin. Intrduce and explain the bilgical principles and/r cncepts that are being investigated. Exhibit an independent understanding f what yu did in lab and why yu did it. Prvide the scientific name f the rganism being investigated (Genus species). State the PROBLEM QUESTION (PQ). Be sure yur prblem questin is fcused enugh s that it specifically states what was under investigatin in the experiment. If a cntrlled experiment was dne, the manipulated and respnding variables must be clearly identified. Often, but nt always, written as, What is the effect f MV n RV? In the case f a true experiment, yu need t explain what yu changed between grups, the Manipulated Variable. Indicate the manipulated variable and list the levels f the MV that yu included in yur experimental prtcl. Prvide the unit fr yur MV. Typically yu shuld have a minimum f 5 levels f the MV. Explain hw the range f levels f yur MV was selected. If yu perfrmed a descriptive study, explain why n variable was r culd be manipulated. Yu need t explain what was measured, the Respnding Variable. List what was measured (bth qualitative and quantitative data) and explain hw it was measured. Prvide the unit fr yur RV. If n qualitative data was cllected, say s, and explain why qualitative data was nt gathered. Fr true experiments in which yu are determining the effect f a MV n and RV, yu need t include a hypthesis. A hypthesis is like a predictin. It will ften take the frm f a prpsed relatinship between tw r mre variables that can be tested by experiment. Hypthesis statements are ften written as: If describe MV_manipulatin, then explain expected result n the RV. Yu must als prvide an explanatin fr yur hypthesis. This shuld be a brief discussin (paragraph frm) abut the science behind yur hypthesis and predictin. Yu shuld site credible references that supprt yur explanatin (see sectin n citatins)

2 DESIGN ASPECT 2: Cntrlling Variables At least three CONTROLLED VARIABLES are required, but mre may be necessary. The cntrlled variables yu list must be relevant t yur investigatin. Yu need t cntrl fr all variables that may reasnably affect the utcme f the investigatin. Materials used and measurement techniques are NOT cntrlled variables (they are validity measures). While materials and techniques must be cnsistent, a true variable is smething that culd directly influence the respnding variable, nt just hw it is measured. Yu must explain why and hw variables were cntrlled. When explaining why a variable needs t be cntrlled, describe hw the variable culd impact the results if it was nt cntrlled. Often times, students create a table t rganize this infrmatin: CONTROLLED VARIABLES WHY in must be cntrlled HOW it was cntrlled DESIGN ASPECT 3: Develping a Methd fr Cllectin f Data Make a list f the MATERIALS needed in the investigatin. Be as specific as pssible (example: 50 ml beaker instead f beaker ); include the vlumes f tubes and cylinders, the cncentratins f slutins, the mdel and manufacturer f any cmplex apparatus. If yu have t decide hw much f a substance r a slutin t use, state yur reasning r shw the calculatins. Include a DIAGRAM OR PHOTOGRAPH f hw yu set up the experiment. Be sure yur diagram includes a title and any necessary labels. It is recmmended that this be anntated t illustrate hw the variables were invlved. State r discuss the PROCEDURE that yu used in the experiment. Be sure yur prcedure explains hw yu changed the manipulated variable. This can be in paragraph frm r a list f step-by-step directins. Prvide enugh detail s that anther persn culd repeat yur wrk by reading yur reprt. If yu use a knwn, published prtcl than yu must prvide a full citatin as a reference. Yur prcedure must include at least three clear VALIDITY MEASURES (i.e. cleaning test tubes prir t use, cleaning the micrscpe lenses, using the same ruler, etc). Validity measures are things kept cnstant t make sure experimental measurements are valid and cnsistent. Yur prcedure must CLEARLY STATE HOW YOU COLLECTED DATA. What measuring device did yu use, what data did yu recrd, when did yu cllect data? What qualitative bservatins did yu lk fr? Explain hw yu set up s yu had MULTIPLE TRIALS f data cllectin. The prcedure must allw cllectin f sufficient relevant data. The definitin f sufficient relevant data depends n the cntext. The planned investigatin shuld anticipate the cllectin f enugh data s that the prblem questin can be suitably addressed and an evaluatin f the reliability f the data can be made. As a rule, the lwer limit is a sample size f five. Very small samples run frm 5 t 20, small samples run frm 20 t 30, and big samples run frm 30 upwards. Obviusly, this will vary within the limits f the time available fr an investigatin.

3 If yu will be COMBINING DATA with data cllected by ther students in the class, yu shuld indicate that, pling data was dne t ensure cllectin f significant, relevant data (IB Bilgy subject guide, 2009, page 26). Be sure t cite this reference if yu pl data. If yu are SAMPLING nly a prtin f a ppulatin, yu must explain hw and why yu ensured that the sample was randmly selected. Yur prcedure must be safe and ethical. Organisms, including humans, can nt be subject t harm in yur investigatin. List any SAFETY PRECAUTIONS that were taken during the lab. If necessary, address the IBO animal experimentatin plicy. DATA COLLECTION AND PROCESSING ASPECT 1: Recrding Raw Data Create a frmal DATA TABLE in which t present the raw, unmdified data yu cllected. Be sure yur table: Is easy t understand Has a specific title Tables are titled in sequential rder as Table 1: title. Table 2: title Has clumn headings Includes the unit f measurement f the MV and RV (always in metric units) Includes the measurement uncertainty f the measurement tls used (r, if the data was a cunt, indicates that cunts have n measurable uncertainty ). Uncertainly is usually stated in a clumn heading r as a ftnte at the bttm f the table. Has a cnsistent and crrect number f digits fr each measurement Has decimal pints aligning dwn a clumn (if applicable) and numbers centered in the clumn Indicates which data was cllected by which student IF the data was cllected and pled acrss multiple students. Yur reprt must include QUALITATIVE DATA. This might be a paragraph in which yu describe the qualitative bservatins and results in general r be specific qualitative data fr each trial that is presented in table frm.

4 LAB DRAWINGS are cnsidered data by the IB Organizatin. Nt all labs will include a lab drawing. Hwever, when included, please be sure yur lab drawings: Are dne with a sharp pencil line n white, unlined paper. Have the drawing ccupy at least half a page, centered n the page. Include labels written ff straight, hrizntal lines t the right f the side f the drawing. The labels shuld frm a vertical list. Are accurate. Draw what yu see; as yu see it, nt what yu imagine shuld be there. Include a title that states what has been drawn and what lens pwer it was drawn under. The title must be infrmative, centered, and larger than ther text. Has a scale that indicates hw many times larger the drawing is cmpared t life size and a scale line that indicates relative size. DATA COLLECTION AND PROCESSING ASPECT 2: Prcessing Raw Data STATISTICS are useful mathematical tls which are used t analyze data. Cmmn statistics used in bilgy are: Mean Range Median Percent change Standard deviatin (t determine amunt f variatin arund a mean) T-test (t cmpare tw means t determine if they are statistically different frm each ther). When a t-test is calculated, yu must indicate the significance level at which yur critical T value is determined (we typically use the 95% cnfidence interval, 0.05). Chi-square (t determine if bserved results are significantly different frm expected results) Crrelatin cefficient (t determine the extent tw variables are related t each ther). Use nly the statistical tests apprpriate t investigate and address yur prblem questin. Fr each statistic yu calculate, yu must EXPLAIN WHY YOU ELECTED TO DO THAT CALCULATION. What des the calculatin tell yu abut the data? DATA COLLECTION AND PROCESSING ASPECT 3: Presenting Prcessed Data Shw an EXAMPLE CALCULATION fr each statistic yu calculate. Use plenty f rm; make sure they are labeled, are clear and are legible. Shw the units f measurements in all calculatins. Pay attentin t the number f digits! Dn t lse accuracy by carelessly runding ff. Rund nly at the end f a calculatin. D nt truncate. Present yur data prcessing results in a TABLE. The initial raw data and the prcessed (calculated) data may be shwn in ne table prvided they are clearly distinguishable. Be sure yur prcessed data table: Is easy t understand Has a specific title Has clumn headings Includes the unit f measurement Has a cnsistent and crrect number f digits fr each measurement (t the same precisin as yur raw data) Has decimal pints aligning dwn a clumn (if applicable) and numbers centered in the clumn Yu must als present yur results in a GRAPH. Use the crrect type f graph fr the type f data yu are presenting. Graphs must be clear and easy t understand. Please avid creative r funny clring f graphs. Graphs need t have apprpriate scales, labeled axes with units, and accurately pltted data pints.

5 Graphs are titled in sequential rder as Figure 1: title. Figure 2: title If necessary, add smth lines r curves t shw the verall trend f the data. If a mean is calculated, nly graph the mean, nt all data pints. When a mean is graphed, its assciated standard deviatin errr bar must als be included (and labeled as such). Legends (keys) are nt always necessary. Delete series 1 and series 2 bxes frm graphs created in Excel. CONCLUSION AND EVALUATION ASPECT 1: Cncluding Write ne (r mre) paragraphs in which yu DRAW CONCLUSIONS FROM YOUR RESULTS. Yur cnclusin shuld be clearly related t the research questin and the purpse f the experiment. Answer the prblem questin (if yu used a T-test, be sure yur cnclusin matches what the T-test tells yu; dn t say there is a difference if the T-test says the difference is insignificant). Was yur hypthesis supprted r refuted? Use the apprpriate language, i.e. Supprts my hypthesis (nt prves r is crrect ). Prvide a brief explanatin as t hw yu came t this cnclusin frm yur results. In ther wrds, sum up the evidence and explain bservatins, trends r patterns revealed by the data. Summarize the prcessed data: mean, range and standard deviatin. Refer directly t tables and graphs by referencing tables and figures (i.e. as seen in Figure 1 ) Summarize the results f the T-test: was the effect f the MV significant r nt? If pssible, CITE LITERATURE related t yur cnclusin. Des yu result cincide with published results? Des it refute published results? CONCLUSION AND EVALUATION ASPECT 2: EVALUATING PROCEDURE In general, hw much CONFIDENCE d yu have in the results? Avid giving yur cnfidence as a percentage; use wrds such as very r smewhat. Are yur results fairly cnclusive, r are ther interpretatins/results pssible?

6 Why are yu (r aren t yu) cnfident? What did yu d t make sure yur results are valid? Was the range f the MV levels apprpriate? Was the data cllected relevant t the prblem questin? Explain any anmalus data pints Identify and discuss significant ERRORS that actually affected yur data cllectin. Yu must identify the surce f errr and tie it t hw it likely affected yur results. Avid hypthetical errrs ( culd have r I might have ) withut evidence t back it up. Cmmn errrs include: Human errr: Human errr can ccur when tls r instruments are used r read incrrectly. Human errrs can be systematic because the experimenter des nt knw hw t use the apparatus prperly r they can be randm because the pwer f cncentratin f the experimenter is fading. Autmated measuring using a data-lgger system can help reduce the likelihd f this errr; alternatively yu can take a break frm measuring frm time t time. D nt list time cnstraints r time management as errrs - they shuld be eliminated with gd practical skills. The fcus here shuld be n the investigatin. Calibratin errr: Sme instruments need calibrating befre yu use them. If this is dne incrrectly it can increase the risk f systematic errr. Randm errrs: In bilgical investigatins, the changes in the material used r the cnditins in which they are carried ut can cause a lt f errrs. Bilgical material is ntably variable. The act f measuring: Culd the measurement uncertainty have affected the results? Why r why nt? Uncntrlled variables: What variables were nt cntrlled? What effect might each f these uncntrlled variables have had n yur data? On the cnclusin? Systematic errrs: culd the measurement uncertainty have affected the results? Why r why nt? Did systematic errrs affect the data? The cnclusin? Errrs and their effect n the results can be clearly presented in a table. What are the LIMITATIONS f yur cnclusin? Can the results be generalized t ther situatins/cnditins? Hw might yur results explain a prcess in the real wrld? CONCLUSION AND EVALUATION ASPECT 3: IMPROVING THE INVESTIGATION What culd yu d t make IMPROVEMENTS t the investigatin? Suggestins fr imprvements shuld be based n the weaknesses and limitatins identified in aspect 2. As apprpriate, address mdificatins t the experimental technique and the data range. Prpse nly realistic and specific mdificatins. Mre time and be mre careful are inadequate.

7 REFERENCES AND CITATIONS It is permissible in the design and cnclusin sectins t use brief qutatins. Smetimes a bk r reference has a phrase r sentence that expresses exactly the thught yu are trying t cnvey; yu may use that phrase r sentence IF yu use qutatin marks and cite a reference at the end f the sentence. It is NOT apprpriate t brrw extensive passages (mre than tw sentences) frm a text r web site. Yu shuld als acknwledge where ideas r knwledge nt riginally yur wn cme frm, even if yu state yur understanding f the idea in yur wn wrds. This is usually dne by putting the first authr s last name and the date f the paper, r the page f a textbk, in parentheses at the end f the sentence cntaining the idea. Any surce yu mentin in the text f yur paper shuld be included in a list f references in a separate sectin at the end f the paper. These references are usually listed in alphabetical rder by the first authr s last name. Make sure all the authrs f a paper r bk are listed, and include the title f the bk r article, the jurnal r publisher (and place), and the date. If yu used just part f a bk, indicate the chapter r pages used. Fr web sites, give the exact electrnic address and any ther infrmatin yu have abut it (the authr, the name f the rganizatin that spnsrs the site). Examples: Bk: Authr(s). Year. Title. Lcatin: Publisher. Number f pages, r pages cited. Hille, Bertil Inic Channels f Excitable Membranes. Secnd Editin. Sunderland, MA: Sinauer Assciates, Inc. 607p. Article: Authr(s). Year. Title f Article. Jurnal, vlume number, pages. Huxley, A.F. and R. Stämpfli Evidence fr salutatry cnductin in peripheral myelinated nerve fibres. J Physil. (Lnd.) 108: Web page: Name f web page. Creatr r publisher. Subject. Web address. The Animated Brain. Brainviews, Ltd. Saltatry cnductin. Lecture r infrmatin frm a teacher. Name f teacher(alphabetically, by last name). The exact date and tpic f the lecture (including the curse in which it was given). Or fr individual answers t questins yu asked a teacher, yu can call it persnal cmmunicatin and give the date. D nt use Wikipedia as a resurce site; hwever yu may read it t gain understanding. MANIPULATIVE SKILLS Labratry skills are assessed summatively ver the curse f the entire IB Bilgy curse. Yur teacher will be watching t see if yu: Fllw directins carefully D nt fabricate data Seek assistance when apprpriate (independence is encuraged) Cnsistently carry ut prper safety measures Effectively use a variety f bilgical techniques Prperly use experimental equipment Safely dispse and reduce waste Wrk in the lab in a way that des nt put yurself r thers in harms way Fllw the IBO animal experimentatin plicy