Good morning!  Thirty men and only two women, but they hold the most power. Dressed in black and white, they could fight for hours.  What are we?

Unit 3 Investigative Biology

Success Criteria  Explain the difference between the dependent and independent variable.  Explain the difference between a negative and positive control and the occasion each would be used.  Explain confounding variables and give an example.  Describe one method to minimise the effect of a confounding variable.  Explain the difference between correlation and causation.  Explain the term multifactorial study in terms of the independent variable.  Explain the difference between in vivo and in vitro studies.  Link qualitative and quantitative data to discrete and continuous variables.  Explain ranked data...  Explain the difference between the dependent and independent variable.  Explain the difference between a negative and positive control and the occasion each would be used.  Explain confounding variables and give an example.  Describe one method to minimise the effect of a confounding variable.  Explain the difference between correlation and causation.  Explain the term multifactorial study in terms of the independent variable.  Explain the difference between in vivo and in vitro studies.  Link qualitative and quantitative data to discrete and continuous variables.  Explain ranked data...

Variables  The independent variable is the factor we change.  The dependent variable is the factor that will be affected by changing the independent variable – it depends on us to measure it!  By varying the independent variable and monitoring its effect on the dependent variable we seek to prove or disprove the hypothesis.  To determine the validity of any change in the dependent variable a control is used.

Controls  A control is a parallel treatment in which the factor being investigated is kept constant.  Controls can be either negative or positive…

Negative controls  Negative controls anticipate no change in the dependent variable.  A good example is the addition of denatured enzyme to substrate when studying the effect of that enzyme on the substrate.  Any change in the dependent variable in the control invalidates the same degree of change in the experimental treatment as it would not be caused by enzyme activity.  This does not mean the whole experiment is invalidated, simply that the effect cannot solely be attributed to changing the dependent variable.  Such controls are used to negate false positive results.

 A negative control group is a control group that is not exposed to the experimental treatment or to any other treatment that is expected to have an effect.  A positive control group is a control group that is not exposed to the experimental treatment but that is exposed to some other treatment that is known to produce the expected effect.  For example, imagine that you wanted to know if some lettuce carried bacteria. You set up an experiment in which you wipe lettuce leaves with a swab, wipe the swab on a bacterial growth plate, incubate the plate, and see what grows on the plate.  As a negative control, you might just wipe a sterile swab on the growth plate. You would not expect to see any bacterial growth on this plate, and if you do, it is an indication that your swabs, plates, or incubator are contaminated with bacteria that could interfere with the results of the experiment.  As a positive control, you might swab an existing colony of bacteria and wipe it on the growth plate. In this case, you would expect to see bacterial growth on the plate, and if you do not, it is an indication that something in your experimental set-up is preventing the growth of bacteria. Perhaps the growth plates contain an antibiotic or the incubator is set to too high a temperature.  If either the positive or negative control does not produce the expected result, it indicates that the investigator should reconsider his or her experimental procedure.

A negative control in action  Hydrogen peroxide degrades to water and oxygen gas on the application of heat.  Catalase is an enzyme that can speed up the breakdown of hydrogen peroxide.  To investigate the action of catalase on hydrogen peroxide at different temperatures, what would your negative control be?  Hydrogen peroxide is exposed to the same temperatures as the experimental treatment but without the presence of the active enzyme.  Any effect different to that shown by the control in the experimental treatment must be caused by the enzyme.

Positive controls  A positive control group is a control group that is not exposed to the experimental treatment but that is exposed to some other treatment that is known to produce the expected effect.  A positive control receives a treatment or test with a known result. This result is usually what researchers expect from the treatment, so it gives them something to compare.

Got a headache yet?!

Ouch my head...  How did doctors figure out if aspirin really cures headaches?  The doctors gave aspirin to someone with a headache and observed if it went away. But...  How do they know the aspirin actually caused the headache to go away?

 She finds a group of people with headaches and gives them some aspirin.  How does she know that it was the aspirin that actually cured the headache?  I've had plenty of headaches that just go away on their own!

Its going away now...  The doctor goes and finds another group of people with headaches, and gives them something that she knows will cure the headache.  She then observes how quickly the headaches go away and how many headaches the known drug cured.  She now has something to compare the aspirin to.  The positive control gives scientists a known to compare to the unknown test.

GOOD MORNING  Four people need to cross a bridge in 17 minutes in the middle of the night. The bridge can only hold two or less people at any time and they only have one flashlight so they must travel together (or alone). The flashlight can only travel with a person so every time it crosses the bridge it must be carried back. Tom can cross in 1 minute, John can cross in 2 minutes, Sally can cross in 5 minutes, and Connor can cross in 10 minutes. If two people cross together they go as fast as the slower person.  How can they cross the bridge in 17 minutes or less?

 First Tom and John will cross (2 minutes). Then Tom will bring the flashlight back (1 minute). Next Sally and Connor will cross (10 minutes). Then John will bring the flashlight back (2 minutes). Finally John and Tom will cross (2 minutes). 2 + 1 + 10 + 2 + 2 = 17 minutes.

GOOD MORNING  Two people are born at the same moment, but they don't have the same birthdays. How could this be?

Confounding variables  In a perfect experiment all possible factors are kept constant and tightly controlled and the only factor that is changed is the independent variable.  Biology is not always that simple – other variables outside the independent variable may potentially affect the independent variable.  Such variables are called confounding variables.

Confused or confounded?!  Any change in a confounding variable may affect the validity of any observed change in the dependent variable.  Anomalous experimental results are often dismissed as ‘the experiment hasn’t worked’.  There are no wrong results just results that don’t yet have an explanation.  A better explanation is that the results may just tell you something you weren’t expecting or that confounding variables were not taken into account.

Spot the confounding variable  In the 1970’s it was hypothesized by the Swedish scientists Haag and Daasz that murder rates increase when ice cream consumption increases.  Does ice cream incite murder or does murder increase the demand for ice cream?

 Neither: they are joint effects of a confounding variable, namely, hot weather.  Another look at the sample shows that it failed to account for the time of year, including the fact that both rates rise in the summertime.

Blocking  When comparing groups of individuals more complex data can be encountered.  Blocking is a practice whereby if confounding variables cannot be controlled their effects are minimised by selecting control and experimental groups in which the effects of confounding variables are equal.

Blocking  You have been asked to determine if classical music has an effect on the ability to complete a mathematical task. What would be the independent variable? ○ The independent variable is music or no music. How would you control the independent variable? ○ Picking one piece and playing it at constant volume for the same length of time. What would be the confounding variables within the sample group?: ○ age ○ gender ○ mathematical ability etc  These variables are difficult to control so you could block the groups…

Blocking Gender Treatment Classical musicNone Male 50 Female 50

Blocking  Both the control and experimental groups should have the same ranges of ages, gender and mathematical abilities.  The number of participants should be the same in both the control and experimental groups.

Matched Pairs  A matched pairs design is a special case of the randomized block design.  It is used when the experiment has only two treatment conditions and participants can be grouped into pairs, based on some blocking variable.  Then, within each pair, participants are randomly assigned to different treatments.  For example, Pair 1 might be two women, both age 21. Pair 2 might be two men, both age 21, Pair 3 two women aged 22 and so on...

A true independent variable?  Simple laboratory-based experiments or even complex studies of people can have easily defined independent variables, but what if there is no true independent variable?  It may appear that studying the effect of gender on a factor such as mathematical ability or creativity will have an independent variable in gender.  This is not quite the case…  The experimenter may find a statistically significant difference between gender and the dependent variable but this may simply show correlation, not causation.  A correlation is detected but causation is not confirmed.

Good afternoon!  A young woman is attending her mother's funeral. While there, she meets a man she has never seen before and falls in love immediately. After the funeral she tries to find him but cannot. Several days later she kills her sister.  Why does she kill her sister?

One or more independent variables?  How can an experiment have more than one independent variable?!!  Take, for instance, the effect of drugs on human physiology.  Many drugs alter their effect when combined with other therapies. While the effect of one drug on its own may provide a single independent variable this is less useful if the drug is usually used in combination with one or more other drugs.

Multifactorial study  Each drug in this case is a factor. When factors are studied in combination this is called a multifactorial study.  The challenge to the experimenter is to generate enough test groups to test every combination of drug factors and doses.  Simply testing one drug on a laboratory model may be easily controlled but its relevance may be limited in vitro…

Vivo vs. Vitro  In vitro (Latin for within the glass) refers to the technique of performing a given procedure in a controlled environment.  In vivo (Latin for “within the living”) refers to experimentation using a whole, living organism  Animal studies and clinical trials are two forms of in vivo research.  In vivo testing is often employed over in vitro because it is better suited for observing the overall effects of an experiment on a living subject.

Discrete Variables  Variables with discrete points as possible values.  Graphed as bar charts defining the variability of a factor amongst discrete groups.  e.g. the data on the effect of gender on mathematical ability would consist of two discrete male and female bars with a numerical value of average mathematical ability on the y- axis.

Continuous variables  Variables where the scale is continuous..  Graphed as line plots, x-y scatter plots or frequency histograms.  e.g. the analysis of the effect of age on hearing range would consist of age as a scaled x-axis and hearing range in Hz on the y-axis.  In any graphing of experimental data the convention is to graph the independent variable on the x-axis and the dependent variable on the y-axis.

Data  Qualitative data The values collected are not numerical or do not show a range of effect. The classic iodine solution test for starch is an example of the generation of qualitative data: starch either is present or it is not present. No estimate of the quantity of starch is possible without further experimentation.  Quantitative data The values collected indicate a numerical value. 10 cm 3 of O 2 is liberated per minute with 0.1% catalase whereas 1 cm 3 is liberated per minute with 0.01% catalase.

How would you rank...  The following Pizza places? Rate 1 the highest...  Ranked data This is where a numerical rank, eg 1st, 2nd, 3rd, is applied to a test situation, eg a group of subjects are asked to rate their preference for an item or situation on a point scale. PizzaRankMean Dominoes Pizza Express Pizza Hut Papa Johns Tony Macaroni

Good morning!  Thirty men and only two women, but they hold the most power. Dressed in black and white, they could fight for hours.  What are we?

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Good morning!  Thirty men and only two women, but they hold the most power. Dressed in black and white, they could fight for hours.  What are we?

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