In the case of environmental effects on children and how it affects their health, it being unethical to actually try an experiment, then from what I gather the best way to test a hypothesis would be to say, "When children are exposed to X, Y will happen." and then to collect data. Would legitimate studies only research this from children that are enrolled in such a study at birth with doctors/scientists/researchers taking blood samples or personally investigating the home environment for these factors? Or could this data be reported by parents? How would you account for people who are dishonest on their reports (as Gregory House states in nearly every episode, "Everybody lies.")? Is a retroactive study considered legitimate if children aren't entered into the study until they are older in age? Or if the researcher only relies on the parents' reporting data from birth to the current age that the child is involved in testing a hypothesis?

A lot of researches are done through surveys. Although it wouldn't be the most accurate, if having someone to check if children are exposed to X or if Y is happening is not possible due to financial, privacy, etc. reasons, survey would be the alternative method. there is even a section on wikipedia of this: https://en.wikipedia.org/wiki/Survey_(human_research)#Reported_behavior_versus_actual_behavior

when the coral reefs are bleached does that mean they have died, if not do they stay white forever or return to their original form?

When a coral is bleaching, it expels symbiotic algae (zooxanthellae), which supply food for the coral, get rid of metabolic byproducts, protect it from pathogens and also create its bright colors. Bleaching results in starvation, which is indicated by loss of color. This doesn't mean that they have died - if the corals find a different nutrient-supplying species, they can regenerate and stabilize again. If they don't find new symbionts and the factors which contribute to bleaching persist, then the coral might die of starvation or disease. A colony of corals might regenerate if corals or their polyps from other colonies migrate and reproduce successfully elsewhere, where the conditions for growth are more favorable.

So hypotheses are like conditional statements? Thanks to anyone who answers, Jack.

Yep! That's a great way of thinking about it. You could write your hypothesis as an if-then statement. :)

why not test different acidity levels with different temperatures?

If each one had different temperatures, then you have too many variables. Let's say you did the same experiment as the Australian researchers, but also had each sample at a different temperature. Let's say you get the following results: Low acidity with cold temperature: 10% bleached Medium acidity with mild temperature: 30% bleached High acidity with hot temperature: 50% bleached Now that you have your results, you know what causes bleaching in coral. But, do you? Is it temperature that causes it? Is it the acidity of the water? Is it the combination of both? How can you tell? This causes confusion and possibly false claims. If you want to know both, it is better to have two seperate tests. One for "Is Acidity Causing Coral Bleach" and another for "Is Temperature Causing Coral Bleach". At the end, you will have two results, two claims, and zero variable confusion. I hope this answers your question! -Colin

How many times do you need to repeat an experiment in order to see if your hypothesis is nearly true or not? Also how big does the sample size need to be to conduct the experiment? 10,20,100?

There are no official rules about how many times experiments should be repeated to be reliable. However, the replication level of your experiments will have an impact on the statistical tests you can perform on your dataset (parametric or non-parametric tests, which do not have the same power). If the aim is to determine whether an event has occurred (for example, whether a pathogen is present in a colony of animals), the number of animals that need to be tested or produced is given by Image p20007f55g177001.jpg where β is the probability of committing a Type II error (usually 0.10 or 0.05) and p represents the proportion of the animals in the colony that are not infected. *Sample size depends on type of experiment.* Here is paper with formula: https://www.ncbi.nlm.nih.gov/books/NBK43321/

What is another example of an everyday use of controlled experiments?

if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others.

How do the sprouting seeds know to go up if they don't know which way the light is?

Plants are PHOTOTROPIC I.e, they sense light and act accordingly. Some plants are negatively phototropic ( go against light) while others are positively phototropic (go towards light). There is also GEOTROPISM, there plants that are negatively geotropic, go against gravity (upwards) while some others are positively geotropic ( the go towards the gravity). These phenomenons come under the topic TROPISM

in the results obtained how does the subtraction of the control affect the results and why

Removal of control would affect results. It would not be measurable. We include controls (positive and/or negative) in order to have something to coampare to and make conclusion).

They grow by doing photosynthesis.(The taking in of carbon dioxide and water where the water is oxidised and when the carbon is reduced, causing a reaction where the carbon dioxide turns into glucose and the water turning into oxygen.)

Main content

Course: Biology archive > Unit 1

Lesson 2: The science of biology

Controlled experiments

Google Classroom

How scientists conduct experiments and make observations to test hypotheses.

Introduction

Biologists and other scientists use the scientific method to ask questions about the natural world. The scientific method begins with an observation, which leads the scientist to ask a question. They then come up with a hypothesis, a testable explanation that addresses the question.

A hypothesis isn't necessarily right. Instead, it's a "best guess," and the scientist must test it to see if it's actually correct. Scientists test hypotheses by making predictions: if hypothesis

X

is right, then

Y

should be true. Then, they do experiments or make observations to see if the predictions are correct. If they are, the hypothesis is supported. If they aren't, it may be time for a new hypothesis.

How are hypotheses tested?

When possible, scientists test their hypotheses using controlled experiments. A controlled experiment is a scientific test done under controlled conditions, meaning that just one (or a few) factors are changed at a time, while all others are kept constant. We'll look closely at controlled experiments in the next section.

In some cases, there is no good way to test a hypothesis using a controlled experiment (for practical or ethical reasons). In that case, a scientist may test a hypothesis by making predictions about patterns that should be seen in nature if the hypothesis is correct. Then, they can collect data to see if the pattern is actually there.

Controlled experiments

What are the key ingredients of a controlled experiment? To illustrate, let's consider a simple (even silly) example.

Suppose I decide to grow bean sprouts in my kitchen, near the window. I put bean seeds in a pot with soil, set them on the windowsill, and wait for them to sprout. However, after several weeks, I have no sprouts. Why not? Well...it turns out I forgot to water the seeds. So, I hypothesize that they didn't sprout due to lack of water.

To test my hypothesis, I do a controlled experiment. In this experiment, I set up two identical pots. Both contain ten bean seeds planted in the same type of soil, and both are placed in the same window. In fact, there is only one thing that I do differently to the two pots:

One pot of seeds gets watered every afternoon.
The other pot of seeds doesn't get any water at all.

After a week, nine out of ten seeds in the watered pot have sprouted, while none of the seeds in the dry pot have sprouted. It looks like the "seeds need water" hypothesis is probably correct!

Let's see how this simple example illustrates the parts of a controlled experiment.

Control and experimental groups

There are two groups in the experiment, and they are identical except that one receives a treatment (water) while the other does not. The group that receives the treatment in an experiment (here, the watered pot) is called the experimental group, while the group that does not receive the treatment (here, the dry pot) is called the control group. The control group provides a baseline that lets us see if the treatment has an effect.

Not necessarily. In general, a controlled experiment must always have a control group as a baseline. However, there may be several experimental groups, each with a slightly different treatment applied to it.

For instance, we could have a control group of bean seeds that got no water, plus three experimental groups: one that got water every day, one that got water every two days, and one that got water once a week.

Independent and dependent variables

The factor that is different between the control and experimental groups (in this case, the amount of water) is known as the independent variable. This variable is independent because it does not depend on what happens in the experiment. Instead, it is something that the experimenter applies or chooses him/herself.

Independent variables

Experimental results are much more straightforward to interpret and analyze when there is just one independent variable (one factor changed at a time). As a general rule of thumb, especially when you are starting out in biology, you should limit yourself to one independent variable per experiment.

Once you have lots of lab experience and some background in statistics, you can consider doing experiments with two independent variables at once. For example, you might want to see how water and light levels jointly affect bean seed sprouting. A well-designed experiment with two independent variables can tell you whether the variables interact (modify each other's effects). However, experiments with more than one independent variable have to follow specific design guidelines, and the results must be analyzed using a special class of statistical tests to disentangle the effects of the two variables.

Dependent variables

Having more than one dependent variable is pretty straightforward. To add a dependent variable, you simply pick another outcome you'd like to measure for each of the groups in your experiment. For instance, in addition to recording the fraction of sprouted bean seeds in each pot, we might also measure the height of the sprouts. In this case, both fraction of seeds sprouted and height of the sprouts would be dependent variables.

In contrast, the dependent variable in an experiment is the response that's measured to see if the treatment had an effect. In this case, the fraction of bean seeds that sprouted is the dependent variable. The dependent variable (fraction of seeds sprouting) depends on the independent variable (the amount of water), and not vice versa.

Experimental data (singular: datum) are observations made during the experiment. In this case, the data we collected were the number of bean sprouts in each pot after a week.

Variability and repetition

Out of the ten watered bean seeds, only nine came up. What happened to the tenth seed? That seed may have been dead, unhealthy, or just slow to sprout. Especially in biology (which studies complex, living things), there is often variation in the material used for an experiment – here, the bean seeds – that the experimenter cannot see.

Because of this potential for variation, biology experiments need to have a large sample size and, ideally, be repeated several times. Sample size refers to the number of individual items tested in an experiment – in this case,

10

bean seeds per group. Having more samples and repeating the experiment more times makes it less likely that we will reach a wrong conclusion because of random variation.

Biologists and other scientists also use statistical tests to help them distinguish real differences from differences due to random variation (e.g., when comparing experimental and control groups).

Controlled experiment case study: ${CO}_{2}$ ‍ and coral bleaching

As a more realistic example of a controlled experiment, let's examine a recent study on coral bleaching. Corals normally have tiny photosynthetic organisms living inside of them, and bleaching happens when they leave the coral, typically due to environmental stress. The photo below shows a bleached coral in front and a healthy coral in back.

A lot of research on the cause of bleaching has focused on water temperature

^{1}

. However, a team of Australian researchers hypothesized that other factors might be important too. Specifically, they tested the hypothesis that high

{CO}_{2}

levels, which make ocean waters more acidic, might also promote bleaching

^{2}

What kind of experiment would you do to test this hypothesis? Think about:

What your control and experimental groups would be
What your independent and dependent variables would be
What results you would predict in each group

Have you given it a try?

Experimental setup

The Australian team collected many fragments of a certain coral species (Acropora intermedia) from the Great Barrier Reef. Then, they split the fragments into three groups, putting each group in water with a different

pH

(acidity level). After eight weeks, the researchers checked each fragment to see how much it had bleached.

Some corals were grown in tanks of normal seawater, which is not very acidic ( $pH$ ‍ around $8.2$ ‍). The corals in these tanks served as the control group.
Other corals were grown in tanks of seawater that were more acidic than usual due to addition of ${CO}_{2}$ ‍. One set of tanks was medium-acidity ( $pH$ ‍ about $7.9$ ‍), while another set was high-acidity ( $pH$ ‍ about $7.65$ ‍). Both the medium-acidity and high-acidity groups were experimental groups.
In this experiment, the independent variable was the acidity ( $pH$ ‍) of the seawater. The dependent variable was the degree of bleaching of the corals.
The researchers used a large sample size and repeated their experiment. Each tank held $5$ ‍ fragments of coral, and there were $5$ ‍ identical tanks for each group (control, medium-acidity, and high-acidity).
Note: None of these tanks was "acidic" on an absolute scale. That is, the $pH$ ‍ values were all above the neutral $pH$ ‍ of $7.0$ ‍. However, the two groups of experimental tanks were moderately and highly acidic to the corals, that is, relative to their natural habitat of plain seawater.

Analyzing the results

When they analyzed their results, the researchers found that corals in the medium-acidity water lost about

20 %

of their color on average, while those in the high-acidity water lost about

40 %

of their color on average. The control samples, in contrast, lost just a little over

10 %

of their color.

This example shows why it is important to have a control group: thanks to the control, the researchers knew that their corals bleached a little in normal water when grown in tanks, but bleached more in water that was extra acidic. Using statistical tests, the researchers found that acidity level had a significant effect on bleaching (that is, that the higher levels of bleaching in the experimental tanks were unlikely to be explained by random variation).

Non-experimental hypothesis tests

Some types of hypotheses can't be tested in controlled experiments for ethical or practical reasons. For example, a hypothesis about viral infection can't be tested by dividing healthy people into two groups and infecting one group: infecting healthy people would not be safe or ethical. Similarly, an ecologist studying the effects of rainfall can't make it rain in one part of a continent, while keeping another part dry as a control.

In situations like these, biologists may use non-experimental forms of hypothesis testing. In a non-experimental hypothesis test, a researcher predicts observations or patterns that should be seen in nature if the hypothesis is correct. They then collect and analyze data, seeing whether the patterns are actually present.

Case study: Coral bleaching and temperature

A good example of hypothesis testing based on observation comes from early studies of coral bleaching. As mentioned above, bleaching is when corals lose the photosynthetic microorganisms that live inside of them, which makes them turn white. Researchers suspected that high water temperature might cause bleaching, and they tested this hypothesis experimentally on a small scale (using isolated coral fragments in tanks)

^{3, 4}

What ecologists most wanted to know, however, was whether water temperature was causing bleaching for lots of different coral species in their natural setting. This broader question could not be answered experimentally, as it wouldn't be ethical (or even possible) to artificially change the water temperature surrounding entire coral reefs.

Instead, to test the hypothesis that natural bleaching events were caused by increases in water temperature, a team of researchers wrote a computer program to predict bleaching events based on real-time water temperature data. For example, this program would generally predict bleaching for a particular reef when the water temperature in the reef's area exceeded its average monthly maximum by

1

^{\circ} C

or more

^{1}

The computer program was able to predict many bleaching events weeks or even months before they were reported, including a large bleaching event in the Great Barrier Reef in 1998

^{1}

. The fact that a temperature-based model could predict bleaching events supported the hypothesis that high water temperature causes bleaching in naturally occurring coral reefs.

Attribution:

This article is a modified derivative of "The science of biology," by OpenStax College, Biology (CC BY 4.0). Download the original article for free at http://cnx.org/contents/185cbf87-c72e-48f5-b51e-f14f21b5eabd@10.4.

The modified article is licensed under a CC BY-NC-SA 4.0 license.

Works cited:

Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res., 50, 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS, 105(45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105.
University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science. Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php.
Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol., 129, 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf.

Additional references:

Coral bleaching. (2016, Feburary 28). Retrieved March 6, 2016 from Wikipedia: https://en.wikipedia.org/wiki/Coral_bleaching.

Hassler, M. (2014, February 19). The difference between independent and dependent variables. [Web log post] In Udemy blog. Retrieved from https://blog.udemy.com/difference-between-independent-and-dependent-variables/.

Independent variable. (2004). In LabWrite. Retrieved from https://www.ncsu.edu/labwrite/po/independentvar.htm.

Multiple independent variables. (2012, December 29). In Psychology research methods: Core skills and concepts. Retrieved from http://2012books.lardbucket.org/books/psychology-research-methods-core-skills-and-concepts/s12-02-multiple-independent-variables.html.

National Center for Education Statistics. (n.d.). What are independent and dependent variables? In Graphing tutorial. Retrieved from https://nces.ed.gov/nceskids/help/user_guide/graph/variables.asp.

National Ocean Service. (2015, October 8). What is coral bleaching? In Ocean facts. Retrieved from http://oceanservice.noaa.gov/facts/coral_bleach.html.

Purves, W. K., Sadava, D. E., Orians, G. H., and Heller, H.C. (2003). Biology is a science. In Life: The science of biology (7th ed., pp. 10-13). Sunderland, MA: Sinauer Associates.

Raven, P. H., Johnson, G. B., Mason, K. A., Losos, J. B., and Singer, S. R. (2014). Science practice 3: Questioning scientifically. In Biology (10th ed., AP ed., p. 9). New York, NY: McGraw-Hill.

Reece, J. B., Urry, L. A., Cain, M. L., Wasserman, S. A., Minorsky, P. V., and Jackson, R. B. (2011). In studying nature, scientists make observations and form and test hypotheses. In Campbell biology (10th ed., pp. 16-21). San Francisco, CA: Pearson.

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Yvonne
Posted 7 years ago. Direct link to Yvonne's post “In the case of environmen...”
In the case of environmental effects on children and how it affects their health, it being unethical to actually try an experiment, then from what I gather the best way to test a hypothesis would be to say, "When children are exposed to X, Y will happen." and then to collect data. Would legitimate studies only research this from children that are enrolled in such a study at birth with doctors/scientists/researchers taking blood samples or personally investigating the home environment for these factors? Or could this data be reported by parents? How would you account for people who are dishonest on their reports (as Gregory House states in nearly every episode, "Everybody lies.")? Is a retroactive study considered legitimate if children aren't entered into the study until they are older in age? Or if the researcher only relies on the parents' reporting data from birth to the current age that the child is involved in testing a hypothesis?
Button navigates to signup pageComment on Yvonne's post “In the case of environmen...”
(95 votes)
Answer
- JayEyeGames
  Posted 7 years ago. Direct link to JayEyeGames's post “A lot of researches are d...”
  A lot of researches are done through surveys. Although it wouldn't be the most accurate, if having someone to check if children are exposed to X or if Y is happening is not possible due to financial, privacy, etc. reasons, survey would be the alternative method.
  
  there is even a section on wikipedia of this:
  https://en.wikipedia.org/wiki/Survey_(human_research)#Reported_behavior_versus_actual_behavior
  Comment on JayEyeGames's post “A lot of researches are d...”
  (36 votes)
Companion1
Posted 8 years ago. Direct link to Companion1's post “when the coral reefs are ...”
when the coral reefs are bleached does that mean they have died, if not do they stay white forever or return to their original form?
Button navigates to signup pageComment on Companion1's post “when the coral reefs are ...”
(28 votes)
Answer
- Herta Elīna Jansone
  Posted 8 years ago. Direct link to Herta Elīna Jansone's post “When a coral is bleaching...”
  When a coral is bleaching, it expels symbiotic algae (zooxanthellae), which supply food for the coral, get rid of metabolic byproducts, protect it from pathogens and also create its bright colors. Bleaching results in starvation, which is indicated by loss of color. This doesn't mean that they have died - if the corals find a different nutrient-supplying species, they can regenerate and stabilize again. If they don't find new symbionts and the factors which contribute to bleaching persist, then the coral might die of starvation or disease.
  A colony of corals might regenerate if corals or their polyps from other colonies migrate and reproduce successfully elsewhere, where the conditions for growth are more favorable.
  Comment on Herta Elīna Jansone's post “When a coral is bleaching...”
  (56 votes)
Jack Smith
Posted 7 years ago. Direct link to Jack Smith's post “So hypotheses are like co...”
So hypotheses are like conditional statements?

Thanks to anyone who answers,
Jack.
Button navigates to signup pageComment on Jack Smith's post “So hypotheses are like co...”
(21 votes)
Answer
- yen nguyen
  Posted 7 years ago. Direct link to yen nguyen's post “Yep! That's a great way o...”
  Yep! That's a great way of thinking about it. You could write your hypothesis as an if-then statement. :)
  Comment on yen nguyen's post “Yep! That's a great way o...”
  (14 votes)
kelly smith
Posted 5 years ago. Direct link to kelly smith's post “why not test different ac...”
why not test different acidity levels with different temperatures?
Button navigates to signup pageButton navigates to signup page
(11 votes)
Answer
- Colin Andrews
  Posted 5 years ago. Direct link to Colin Andrews's post “If each one had different...”
  If each one had different temperatures, then you have too many variables. Let's say you did the same experiment as the Australian researchers, but also had each sample at a different temperature. Let's say you get the following results:
  
  Low acidity with cold temperature: 10% bleached
  
  Medium acidity with mild temperature: 30% bleached
  
  High acidity with hot temperature: 50% bleached
  
  Now that you have your results, you know what causes bleaching in coral. But, do you? Is it temperature that causes it? Is it the acidity of the water? Is it the combination of both? How can you tell? This causes confusion and possibly false claims. If you want to know both, it is better to have two seperate tests. One for "Is Acidity Causing Coral Bleach" and another for "Is Temperature Causing Coral Bleach".
  
  At the end, you will have two results, two claims, and zero variable confusion.
  
  I hope this answers your question!
  
  -Colin
  Comment on Colin Andrews's post “If each one had different...”
  (23 votes)
debwallace74
Posted 5 years ago. Direct link to debwallace74's post “How many times do you nee...”
How many times do you need to repeat an experiment in order to see if your hypothesis is nearly true or not?
Also how big does the sample size need to be to conduct the experiment? 10,20,100?
Button navigates to signup pageComment on debwallace74's post “How many times do you nee...”
(11 votes)
Answer
- Ivana - Science trainee
  Posted 5 years ago. Direct link to Ivana - Science trainee's post “There are no official rul...”
  There are no official rules about how many times experiments should be repeated to be reliable. However, the replication level of your experiments will have an impact on the statistical tests you can perform on your dataset (parametric or non-parametric tests, which do not have the same power).
  
  If the aim is to determine whether an event has occurred (for example, whether a pathogen is present in a colony of animals), the number of animals that need to be tested or produced is given by
  
  Image p20007f55g177001.jpg
  where β is the probability of committing a Type II error (usually 0.10 or 0.05) and p represents the proportion of the animals in the colony that are not infected.
  
  *Sample size depends on type of experiment.*
  
  Here is paper with formula:
  https://www.ncbi.nlm.nih.gov/books/NBK43321/
  Comment on Ivana - Science trainee's post “There are no official rul...”
  (12 votes)
小苹果
Posted 6 years ago. Direct link to 小苹果's post “What is another example o...”
What is another example of an everyday use of controlled experiments?
Button navigates to signup pageComment on 小苹果's post “What is another example o...”
(10 votes)
Answer
- Joann
  Posted 4 years ago. Direct link to Joann's post “if you planted different ...”
  if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others.
  Button navigates to signup page
  (10 votes)
Brian F
Posted 7 years ago. Direct link to Brian F's post “How do the sprouting seed...”
How do the sprouting seeds know to go up if they don't know which way the light is?
Button navigates to signup pageComment on Brian F's post “How do the sprouting seed...”
(9 votes)
Answer
- madhoolika.chodavarapu
  Posted 5 years ago. Direct link to madhoolika.chodavarapu's post “Plants are PHOTOTROPIC I....”
  Plants are PHOTOTROPIC I.e, they sense light and act accordingly. Some plants are negatively phototropic ( go against light) while others are positively phototropic (go towards light).
  
  There is also GEOTROPISM, there plants that are negatively geotropic, go against gravity (upwards) while some others are positively geotropic ( the go towards the gravity).
  
  These phenomenons come under the topic TROPISM
  Button navigates to signup page
  (4 votes)
lefilsdarren
Posted a year ago. Direct link to lefilsdarren's post “Isn't taking coral reefs ...”
Isn't taking coral reefs from their environment unethical since corals are disappearing? Why not create an experiment that would remove co2 levels by adding plants that sequester carbon near the bleached corols? The corols in the water that do not have plants that sequester carbon would be the control group, while the others would be the experimental groups. The independent variables would either be the co2 or pH levels. The dependent variable would be the degree of bleaching from the corals. You would do a side-by-side comparison of each of the waters that have different co2 levels. Then, you could do large/repeated sample sizes by testing waters with high/medium/regular co2 levels that have corals in the water.
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tl6369
Posted a year ago. Direct link to tl6369's post “how do platns grow”
how do platns grow
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(6 votes)
Answer
- Mint
  Posted a year ago. Direct link to Mint's post “They grow by doing photos...”
  They grow by doing photosynthesis.(The taking in of carbon dioxide and water where the water is oxidised and when the carbon is reduced, causing a reaction where the carbon dioxide turns into glucose and the water turning into oxygen.)
  Button navigates to signup page
  (7 votes)
Asithandile Ntsondwa
Posted 6 years ago. Direct link to Asithandile Ntsondwa's post “in the results obtained h...”
in the results obtained how does the subtraction of the control affect the results and why
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(7 votes)
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- Ivana - Science trainee
  Posted 5 years ago. Direct link to Ivana - Science trainee's post “Removal of control would ...”
  Removal of control would affect results. It would not be measurable. We include controls (positive and/or negative) in order to have something to coampare to and make conclusion).
  Comment on Ivana - Science trainee's post “Removal of control would ...”
  (4 votes)

Biology archive

Course: Biology archive > Unit 1

Introduction

How are hypotheses tested?

Controlled experiments

Control and experimental groups

Independent and dependent variables

Variability and repetition

Controlled experiment case study: CO2‍ and coral bleaching

Non-experimental hypothesis tests

Case study: Coral bleaching and temperature

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Controlled experiment case study: ${CO}_{2}$ ‍ and coral bleaching