Chapter 1. Working With Data 29.12

Working with Data: HOW DO WE KNOW? Fig. 29.12

Fig. 29.12 describes Otto Renner’s experiment showing that leaves exert stronger forces on water being pulled from the soil than a vacuum pump could. Answer the questions after the figure to practice interpreting data and understanding experimental design. These questions refer to concepts that are explained in the following three brief data analysis primers from a set of four available on LaunchPad:

Experimental Design
Data and Data Presentation
Statistics

You can find these primers by clicking on the button labeled “Resources” in the menu at the upper right on your main LaunchPad page. Within the following questions, click on “Primer Section” to read the relevant section from these primers. Click on the button labeled “Key Terms” to see pop-up definitions of boldfaced terms.

HOW DO WE KNOW?

FIG. 29.12: How large are the forces that allow leaves to pull water from the soil?

BACKGROUND The idea that water is pulled through the plant by forces generated in leaves was first suggested in 1895. However, without a way to measure these forces, there was no way to know how large they were.

HYPOTHESIS In 1912, German physiologist Otto Renner hypothesized that leaves are able to exert stronger suctions than could be generated with a vacuum pump.

EXPERIMENT Renner measured the rate at which water flowed from a reservoir into the cut tip of a branch of a transpiring plant. He next cut off about 10 cm of the branch and attached a vacuum pump in place of the transpiring plant. He then compared the flow rate through the branch when attached to the transpiring plant with the flow rate generated by the vacuum pump.

RESULTS Renner found that the flow rates generated by transpiring plants were two to nine times greater than the flow rates generated by the vacuum pump.

CONCLUSION A transpiring plant pulls water through a branch faster than a vacuum pump. Therefore, the pulling force generated by a transpiring plant must be greater than that of a vacuum pump. Because vacuum pumps create suctions by reducing the pressure in the air, the maximum suction that a vacuum pump can generate is 1 atmosphere. Thus, the maximum height that one can lift water using a vacuum pump is 10 m (33 feet). The forces that pull water through plants have no comparable limit because they are generated within the hydrated cell wall. This is why plants can pull water from dry soils and why they can grow to more than 100 m in height.

FOLLOW-UP WORK In 1965, Per Fredrick Scholander developed a pressurization technique that uses reverse osmosis to measure the magnitude of the suctions exerted by leaves of intact plants. He used this technique to show that the leaves at the top of a Douglas fir tree 100 m tall exert greater pulling forces than do leaves closer to the ground.

SOURCE Renner, O. 1925. “Zum Nachweis negativer Drucke im Gefässwasser bewurzeler Holzgewachse.” Flora 118/119:402-408.; Scholander, P. F., H. T. Hammel, E. D. Bradstreet, and E. A. Hemmingson. 1965. “Sap Pressure in Vascular Plants.” Science148:339–346.

Question 1 of 11

Question

In the original paper published by Otto Renner, all of the flow rates measured into intact plants were greater than what could be produced by a vacuum pump pulling water through the same branch segment. Under what conditions would you predict that Renner would have measured a lower flow rate into the intact plant than subsequently measured using the vacuum pump?

A plant growing in well-watered soil was measured at night or during a rainstorm.

The plant was growing in very dry soil.

It is never possible for the flow rate into an intact plant to be lower than the flow rate into a vacuum pump.

The plant was exposed to an atmosphere with twice the CO₂ concentration present in the atmosphere today.

Correct.

Incorrect.

Incorrect. Please try again.

	Population A
	Plant 1	Plant 2	Plant 3	Plant 4	Plant 5
Flow_plant (μg/min)	6.3	5.7	4.0	2.1	9.8
Flow_pump (μg/min)	0.3	0.3	0.2	0.1	0.5
	Population B
	Plant 1	Plant 2	Plant 3	Plant 4	Plant 5
Flow_plant (μg/min)	52.3	22.8	18.2	36.7	15.0
Flow_pump (μg/min)	5.0	2.3	1.7	3.8	1.5

Question 4 of 11

Question

You now conduct a statistical test to determine whether the mean value of the pulling force measured for population A ( $\overline{x}$ _A) is equal to the mean value of the pulling force measured for population B ( $\overline{x}$ _B). It may seem strange that we are testing the null hypothesis ( $\overline{x}$ _A = $\overline{x}$ _B) rather than our hypothesis ( $\overline{x}$ _A ≠ $\overline{x}$ _B).
However, as stated in the Experimental Design primer: “Hypotheses are never proven, but it is possible based on statistical analysis to reject a hypothesis.” If there is a low probability that the null hypothesis is correct, we can “reject” the null hypothesis with a level of confidence described by our statistical test, and thereby gain support for our alternative hypothesis.
The P-value for the statistical test is: P < .01. This means that:

the probability that your hypothesis is correct is < 1%.

the probability that the null hypothesis is correct is < 1%.

Correct.

Incorrect.

Incorrect. Please try again.

statistical test	A test used to distinguish accidental or weak relations from real and strong ones.
P-value	The likelihood that an observed result (or a result more extreme than that observed) could have been observed merely by chance. If P ≤ .05, the observed results are conventionally regarded as unlikely to be attributed to chance alone.

Table

Statistics

Statistical Significance

Biologists observe many relations between variables that are either due to chance in the sample that happened to be chosen or that are too weak to be biologically important. To distinguish the accidental or weak relations from the real and strong ones, a statistical test of the relation is carried out. A statistical test must be based on some specific hypothesis. For example, to determine whether an observed correlation coefficient could be due to chance, we might carry out a statistical test of the hypothesis that the true correlation coefficient is 0. A statistical test usually yields a single number, usually called the P-value (or sometimes p-value), that expresses the likelihood that an observed result (such as a correlation coefficient) could have been observed merely by chance. A P-value is a probability, and if P ≤ 0.05 the observed results are conventionally regarded as unlikely to be attributed to chance alone. In that case, the observed relation is likely to be genuine. In other words, if an observed relation would be obtained by chance alone in only 1 in 20 or fewer experiments (P ≤ 0.05), then the observed relation is regarded as likely to be true. A finding of P ≤ 0.01 is taken as even stronger evidence that the observed result is unlikely to be due to chance.

Figure 6

Statistical testing is necessary because different researchers may disagree on whether or not a finding supports a particular hypothesis or if the interpretation of a result could be affected by wishful thinking. Take Figure 6, for example. If you wish to believe that there was a functional relation between xand y, you might easily convince yourself that the 20 data points fit the straight line. But in fact the P value of the regression coefficient is about P = 0.25, which means that about 25% of the time you would get a line that fits the data as well or better than the line you observed, purely by chance. The proper conclusion is that these data give no support for the hypothesis of a functional relation between x and y. If there is such a relation, then it is too weak to show up in a sample of only 20 pairs of points.

There is good reason to be cautious even when a result is statistically significant. Bear in mind that 5% of statistical tests are misleading in that they indicate that some result is significant merely as a matter of chance. For example, over any short period of time, about 5% of companies listed in stock exchanges will have changes in the dollar value of their shares that are significantly correlated with changes in the number of sunspots, even though the correlation is certainly spurious and due to chance alone. Critical thinking therefore requires that one maintain some skepticism even when faced with statistically significant results published in peer-reviewed scientific journals. Scientific proof rarely hinges on the result of a single experiment, measurement, or observation. It is the accumulation of evidence from many independent sources, all pointing in the same direction, that lends increasing credence to a scientific hypothesis until eventually it becomes a theory.

raw data	A series of observations or measurements.
data table	A table presenting data, typically categorized in some way.

Species	A	B	C	D	E	F
Number of sightings	43	47	3	5	7	3

Species	A	B	C	D	E	F
Number trapped	17	29	5	2	5	3