Victor Lau

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1. Bar chart

when X is categorical variable e.g. male/female

2. Short straight lines joining up the points

when you are uncertain about the values between the points

3. Best fit line/trend line/regression line, when:

The data points, when joined with short straight lines, cannot show a clear trend

Theory supports that the change should be smooth and continuous;

You need to estimate the values between or beyond points e.g. water potential of epidermis/potato tissue

http://www.controlledenvironments.org/Light1994Conf/5_11_Bula/Bula%20text.htm

http://www.agnet.org/library.php?func=view&style=type&id=20110729153539

By hand :

Judge if the points look like a straight line or a curve, and if the theory supports that relations.

Draw a line with half of the points on each side, and the average distance of the points to the line on each side is the same

By Excel:

1. Draw scatterplot

2. Add trendline

3. Try different types of trendlines until one that has the largest R2 and supported by theory

Regression line choice http://www.biostathandbook.com/curvilinearregression.html

Biochem J. 2007 Mar 1; 402(Pt 2): 331–337. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1798444/

R2=0.174, P=0.08

R2=0.372, P=0.03

R2=0.765, P=0.0001

How?

Variable Measurement Light intensity • Distance of the table lamp from the

plant • Wattage of the bulb • Brightest detected by photometer

Rate of photosynthesis

Rate of oxygen production measured by : • Rate of bubble production • Time for a leaf disc to float

Vitamin C content

• Detected by DCPIP titration • Detected by iodine titration

Osmosis of the potato tissue

• Change in mass • Change in length • Change in water potential of the

bathing solution

Determine vitamin C content with iodine titration http://chemistry.about.com/od/demonstrationsexperiments/ss/vitctitration.htm#step1

http://www.saps.org.uk/secondary/teaching-resources/286-measuring-the-water-potential-of-a-potato-cell

http://employees.csbsju.edu/ssaupe/biol327/Lab/water/water-lab-chard.htm

What is the dependent variable?

Vitamin C content

How to measure the variable and what is the underlying theory ? Red DCPIP is reduced by vitamin C to become colourless. When fruit juice is titrated by DCPIP solution until it remains red, the vitamin C content in the fruit juice can be calculated.

Any assumptions needed for the variable to be measured by this method?

This method has to assume that there are no other reductants in the juice.

How to reduce the measurement errors?

Several titrations will be done for each juice to reduce the random errors.

The end point is observed by the same person against a standard solution to reduce bias in observing the end point.

The juice will be filtered and diluted first to allow better end point observation.

What are the variables that need to be controlled? How is it controlled? Is the control good enough?

Volume of the fruit juice can be controlled by using graduated pipette to measure a fixed volume. This control is good as the pipette is accurate.

Fruit juice in contact with air may have its vitamin C oxidized. The solution is swirled in a gentle and consistent manner to control its contact with air. But the control may not be good since each time of swirling will differ slightly and some juice needs longer time of titration and thus mix more with air.

What is the hypothesis to test?

The jelly cannot set because the jelly protein is broken down by protease from the pineapple

How is it tested?

Boil the pineapple to denature the protease. This will stop any enzyme activity and the jelly will set when mixed with boiled pineapple.

Is it a good test?

It is not a direct and accurate test for protease.

List all possible errors separately.

Associate the likely errors with the validity of the conclusion

List all possible improvements, including those that have already been done.

Suggest improvements that will significantly improve the validity of the conclusion from the present design.

Describe the theory separately.

Connect theory with the data, and point out where they are in conflict.

1. What conclusion can be drawn from the data?

2. How much confidence do you have in the conclusion? Discuss it with the following:

Are the trends of the data obvious?

Are there any important measurement errors?

Are there any important variables that have not been controlled properly?

Are there any important assumptions that seem unjustified?

Can the conclusion be generalize?

3. What are the improvements suggested for the investigation?

4. What is the significance of the conclusion in everyday life and future research?

討論與结論

實驗结果顯示，薯條的重量改变與蔗糖溶液的濃度呈線性關係，繪畫一直線最適線， 推論薯條在0.32M蔗糖溶液時沒有重量改變。由此，得出结論是，薯條的水勢與0.32M蔗糖溶液相同。

但是，這结論的可信性受下列因素影响:

(Trend)

實驗只有五個数據和用人手繪畫最適線，令這推論不大準確。

(Measurement)

量度上亦有誤差，雖然這些誤差未必對實驗的结論有很大影响：

薯條在放入溶液前，一些水分或會蒸發掉，這會令量度得的水勢較真正的稍低。

薯條浸在溶液中35分鐘可能不足，渗透作用未能達致平衡，這或會令薯條的重量改變減少。

薯條表面的水份可能未能完全拭走，這令薯條的最终重量增加，令量度得的水勢較真正的稍高。

(Unjustified assumption)

實驗假設了薯條重量改变只與渗透作用有關，但水份會渗入细胞間和细胞壁中。

實驗亦假設了蔗糖溶液的濃度不會因渗透作用而改變，但似乎渗出/入的水量不少，這會對實驗的结論有一定影响。

(Generalization)

因實驗只用了一個薯仔，這结論不能概括至其他薯仔上。

實驗可作下列改善:

在0.2M-0.4M蔗糖溶液間取得更多数據，以作更準碓的推論。

減少薯條暴露於空氣的時間和覆蓋着薯條，以減少水分蒸發。

增加薯條浸在溶液中的時間，每半小時量度重量，直至重量不变，顯示渗透作用已達致平衡。

可用蔗糖溶液的濃度改變去显示渗透作用，避免水份渗入细胞間造成的重量誤差。