Biology P5

[MukeshG93]

I got some Protips to help out.
- Whenever there is an experiment that involves measuring quantities and comparing them, mention the use of a statistical test
- Whenever you are to clarify the authenticity of a hypothesis using provided data, the lack of use of a statistical test is usually always a staple point
- Comparative terms include: Bottom of range of X is larger than top of range of Y or the range overlaps (for a contradictory argument

 

[MukeshG93]

- Whenever you are dealing with something relating to the light-dependent stage of photosynthesis, make sure you use a lamp at a fixed distance from the apparatus. Use a bulb of same wattage or use the same light-filter for the whole procedure (This, of course, is if you are NOT investigating the effect of light on photosynthesis)
- Whenever dealing with something relating to the light-independent stage, maintaining the concentration of CO2 in the air is KEY! Mention things like dry ice and what not (This, ALSO, is only if you are not investigating the effect of CO2 on photosynthesis)
- If it is a potometer experiment, mention both of these. Also mention wind-speed because that determines the rate of transpiration which then determines the opening/closure of stomata which then determines the volume of CO2 taken, which would alter the rate of photosynthesis. If repeating the procedure, take shoots with the same number of leaves. Use a gas syringe to measure the uptake of water by the plant.
- For reactions that mention serial dilution, explain how to dilute the solution (what volumes of water and the solution would be used as an example)

 

[MukeshG93]

Here's a list of how to keep a couple of variables constant:
Temperature:
- THERMOSTATICALLY CONTROLLED water bath
- Air conditioned room
- Incubator

Wind speed:
- Use of fan
- Fan placed at same distance
- Same Air speed of fan used

I mentioned Light intensity and Carbon Dioxide concentration in my previous posts.

 

[zeebujha]

Now the ball's rolling! Notch it up guys!!

 

[MukeshG93]

Now the ball's rolling! Notch it up guys!!

Speaking of balls, tips on alginate beads:

- The solution you're mixing the enzyme to be immobilized with is Calcium Chloride
- If you want even sized easy to use beads, mix the solution DROPWISE with the solution containing Sodium Alginate
- The sodium displaces the calcium in CaCl, giving birth to the calcium alginate beads

Using the beads:

- Place them in a column like a large burette and use a stand to hold it over a beaker
- Pour the solution to be broken down by the immobilized enzyme slowly, you want all of it to be broken down so don't rush them through it!
- As the solution with its broken down contents drops down the nozzle of the burette, collect it in the beaker
- Before using the apparatus again, run water through the beads to rinse out any leftover solution

Things to write in the exam:

- When writing about constants, it's better you mention using the same number of beads per trial and pouring the solution in at the same rate per trial.
- Why is conducting enzyme-substrate experiments in this manner more convenient? You don't have to separate the enzymes from the reaction mixture after the substrate has been broken down. The immobilized enzymes are not easily denatured. They can be reused many times.

Anything you need to add Zeebu? Also, you recognize it's me, right? Lol, I was wondering if you'd noticed.

 

[zeebujha]

Now the ball's rolling! Notch it up guys!!

Speaking of balls, tips on alginate beads:

- The solution you're mixing the enzyme to be immobilized with is Calcium Chloride
- If you want even sized easy to use beads, mix the solution DROPWISE with the solution containing Sodium Alginate
- The sodium displaces the calcium in CaCl, giving birth to the calcium alginate beads

Using the beads:

- Place them in a column like a large burette and use a stand to hold it over a beaker
- Pour the solution to be broken down by the immobilized enzyme slowly, you want all of it to be broken down so don't rush them through it!
- As the solution with its broken down contents drops down the nozzle of the burette, collect it in the beaker
- Before using the apparatus again, run water through the beads to rinse out any leftover solution

Things to write in the exam:

- When writing about constants, it's better you mention using the same number of beads per trial and pouring the solution in at the same rate per trial.
- Why is conducting enzyme-substrate experiments in this manner more convenient? You don't have to separate the enzymes from the reaction mixture after the substrate has been broken down. The immobilized enzymes are not easily denatured. They can be reused many times.

Anything you need to add Zeebu? Also, you recognize it's me, right? Lol, I was wondering if you'd noticed.

Of course I know it is you, Mukesh, my man. Men on mission don't interact much, lol

 

[zeebujha]

To determine the extent to which hypothesis is supported by results, consider:
1. The general trend shown by the results
2. Anomalous results
3. Sample size
4. Repeats
5. Range of independent variable for which sample is taken
6. Whether or not tests have been carried out to check the SIGNIFICANCE of the results

 

[zeebujha]

BIO-STATS:
This could have been much better explained by someone who has taken STATS II, but I unfortunately haven't done that. So , you will have to unfortunately bear with and CORRECT my misunderstandings.

First of all, we need to realize that irrespective of how scary the terms such as t-test, chi-squared test blah blah.........., seem all they basically do is test a hypothesis. The whole of biology stats required in our P5 course deals with HYPOTHESIS TESTING

For hypothesis testing we need a hypothesis (duh!)
So, we choose a NULL hypothesis
The hypothesis assumes that there are no significant differences between the means of two different samples. A null hypothesis could be: The marks of Pranav and Mukesh are not different (Or the marks or the same) { For Mukesh: You always score higher than me buddy so this hypothesis is surely gonna be wrong , lol}
All we try to do via statistical tools is to prove that either the null hypothesis is correct or it is wrong.

Along with NULL HYPOTHESIS, we have an ALTERNATIVE HYPOTHESIS. If the null hypothesis is proven we assume that the alternative hypothesis is right. It is to be noted that we do not prove that the alternative hypothesis is right, we just prove that the null hypothesis is wrong!

 

[zeebujha]

Now, let us consider how we can consider how we can prove if the null hypothesis is right or wrong. To do that, we have to check the SIGNIFICANCE OF OUR RESULTS.

Please realize that I am explaining what I write to myself as much as I am explaining it to you
The all important term: SIGNIFICANCE!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

Statistical Significance:
In statistics, a result is called statistically significant if it is unlikely to have occurred by chance. So, the greater the chance that the result did not occur by chance , the greater the significance of the result.

How do we check signficance:
For that we have got:
1. Chi square test
2. T- test
(Yeps for all the horrible terms, these two are basically all we need to know about)

I am not going to explain the meanings of the term SD and Mean, you MUST know them by the end of the whole academic year.

So, gonna jump to Chi-Squared test:

Chi-squared test: It is used for DISCRETE data. The probability obtained as an answer to the chi-squared test states what is the probability that the differences between the expected results and the obtained results WERE ENTIRELY DUE TO CHANCE. If the probability is greater than 95, the DIFFERENCES are significant and the null hypothesis is WRONG. So, if Probability of differences being signficant is HIGH, null hypothesis BYE BYE!!!!!!!!!

 

[zeebujha]

Now comes the monster, the T-TEST:
ALWAYS KEEP IN MIND THAT T-TEST ONLY WORKS FOR CONTINUOUS DATA!!!!!!!!!!!(unlike chi-squared test)

First we need to understand the standard error:
It is the standard deviation of the sampling distribution of the sample mean. This distribution is always a normal distribution irrespective of the distribution of the original sample if n>30 where n is the number of data in each sample





I know it is very confusing , it would be better if you checked out KHAN ACADEMY videos on this !


Whatever, what we need to understand is , the value of standard error tells us how close we would expect the means of any further data sets to lie to the first mean. The smaller the standard error , the more confident we can be that the means of our second, third and so on data sets will produce means close to the original mean.
Standard error tells us that we can be 95% sure (this percentage springs from the property of normal distribution) that , should we the population be sampled again, the new means obtained will be Mean+- (2X Standard error). If two means lie within the given range, this is an indication that the two means are not significantly different.

Standard error is used to calculate confidence limits. These indicate how certain we can be that the true mean of a whole population lies within the range of
the estimated sample mean

T-TEST:
We use the t-test to assess the significance of the difference between the means of TWO sets of data which are expected to belong to a normal distribution

For t-test :
1. TWO means are compared
2. The data has normal distribution
3. There is no overlap between sets of data

THE PROBABILITIES THAT THE TEST PRODUCES ARE PROBABILITIES THAT THE NULL HYPOTHESIS IS CORRECT, AND THERE IS NOT SIGNIFICANT DIFFERENCE BETWEEN THE MEANS OF TWO SAMPLES.

The t-test (as well as the chi-squared test) have what we call critical values. The critical value is a value greater than which all t-values would show that the differences between the two samples are significant. We can think of the t-distribution as a normal distribution when n>30. With the desired t-value(the mean of the normal distribution) to be zero. The further the value of t from the given value , the more uneasy the null hypothesis becomes to digest. There comes one value for which, we have had enough. This value is the critical t-value.

If the total number of observations (both samples added together) is below 30, error due to chance is significant and the table of t makes an adjustment to critical values to take this into account, why is why we need to calculate the HORRIBLE DEGREES OF FREEDOM.

DEGREES OF FREEDOM (WTF!): The degrees of freedom of an estimate is the number of independent pieces of information that go into the estimate. In general, the degrees of freedom for an estimate is equal to the number of values minus the number of parameters estimated en route to the estimate in question. For example, to estimate the population variance one must first estimate the population mean. Therefore, if the estimate ov variance is based on N observations, there are N-1 degrees of freedom. For two samples, each with N samples, the total total number of degrees of freedom in N-1+N-1= N-2

It should be noted that t-test gives us a measure of VALIDITY not RELIABILITY (the extent of reliability is determined by the spread of value about the mean value i.e SD). Also, the t-test only compares two means and thus we can only write comparative statements about those two means. We cannot deduce a conclusion about the whole sample from a result of t-test which compares only 2 results

I know all that did not make sense, but I tried, sigh........................... Mukesh you could use your articulate language to put more sense into these seemingly abstract stuffs!!!!!!!

 

[zeebujha]

Points required for planning:

1. Varying the independent variable:
-Suggest how to vary the independent variable
-How the value of the independent of variable will be measured
-Which values of the independent variable will be used (MENTION AT LEAST FIVE VALUES)

2. Measuring the dependent variable

3. Controlling any 2 variables (you MUST mention the method of controlling) This mainly deals with the accuracy of the experiment

4. Any 2 procedures of using the apparatus

5. How to make the experiment reliable

6. Safety precaution
If no obvious safety precaution required you MUST mention that "THIS IS A LOW RISK EXPERIMENT"

 

[MukeshG93]

Alright, let me give an entirely exam-oriented perspective at these statistical tests:

1) It is just math
2) They give you the formula, there is no memorisation required
3) It assumes you have knowledge on elementary statistics (i.e. mean, standard deviation)
4) It also assumes that you have brought a reasonably functional calculator with you
5) It is an excuse you can use when you are asked to suggest to what extent a given hypothesis is supported by the student's results (trust me, it's always the student that gets the results, they always use the term student in this type of thing, coincidence or lack of originality in CIE's part?)
6) They will give you this table for both tests with "degrees of freedom" and the "probability is greater than". These terms are unnaturally fancy for something this basic and so are the names "chi-squared" and "t-test". What're you trying to do, scare us teens from our pursuit of a career in science?
7) Degrees of freedom means the amount of freedom the data has when we are comparing them. This "freedom" is not the same as the "get out of jail" freedom. Don't think about it too much, it just means, in our sample of data, there is this amount of randomness due to the large/small number of data which we need to keep in consideration when calculating the values for the chi-squared and t probability. (In other words: the more the data, the more we need to consider the spread of it, hence we need to alter the testing values to suit the different numbers of data that may be presented to us) Mathematically, it is just n -1 where n = number of data.
8) Calculate --> Compare --> Reject (and by reject I mean reject the null hypothesis) What a null hypothesis basically is, is the exact opposite statement of what we are testing. If we are testing the difference between to sets of values, we say they are not different, so the tests let us prove ourselves wrong (scientists are so crazy that they love being proven wrong! What a hobby! :no: ). So, we calculate the t and chi-squared values using the formula that is given to us. Compare it with the respective value with the same degrees of freedom in the provided table (if your degree of freedom is not in the table, just find the values it lies between and find the mean of the respective probabilities of the two values, i.e. your degrees of freedom ins 29 but the table only lists values for 28 and 30. Now if the values at 28 and 30 are 2.1 and 2.2, respectively, then the value you're looking for is 2.15! Get it?) I don't remember how the value relates to the rejection or acceptance of the hypothesis but you have Pranav (AKA Zeebu) for that.
9) STAY CALM! This is just a small city of the big country we know as Biology P5, so please, if you don't understand it, stop wasting your time, move on and concentrate on better and more important things!
10) Good day!

 

[MukeshG93]

Points required for planning:

1. Varying the independent variable:
-Suggest how to vary the independent variable
-How the value of the independent of variable will be measured
-Which values of the independent variable will be used (MENTION AT LEAST FIVE VALUES)

2. Measuring the dependent variable

3. Controlling any 2 variables (you MUST mention the method of controlling) This mainly deals with the accuracy of the experiment

4. Any 2 procedures of using the apparatus

5. How to make the experiment reliable

6. Safety precaution
If no obvious safety precaution required you MUST mention that "THIS IS A LOW RISK EXPERIMENT"

This is awesome stuff. Just use your knowledge in Biology to predict how to control the variables and how to measure them. Likewise, the constants must be appropriate to the context. The apparatus will always be something that you have seen or used before so please make sure you know how to use all the lab stuff. Safety precaution, don't be skittish to mention your fears of cutting yourself, burning yourself, corroding yourself (with strong acids) or poisoning yourself (the dangerously volatile ethanol). Yes, mention "THIS IS A LOW RISK EXPERIMENT". As a matter of fact, you can say: "Although there is a risk of cutting yourself while taking a section of <insert appropriate biological content here>, this is a relatively low risk experiment" to get two whole marks. How's that for cheap?

 

[Xam]

help!!!!
oct 07 q3 how do we get E? n no of freedom?
oct 08 q2 b.ii n iv
may 09 q1 b whole

 

[zeebujha]

help!!!!
oct 07 q3 how do we get E? n no of freedom?
oct 08 q2 b.ii n iv
may 09 q1 b whole

buddy you need to be more specific about what confuses you!

 

[zeebujha]

This is not very important but we can never be sure if CIE picks on this :
Be careful about the use of AGAR and AGAROSE:
Use agar in the context of microbiology
Use agarose in the context of gel electrophoresis

 

[honeycoveredcookie]

Can somebody please help me with this particular question.

October / November 2007, Question 3 A.
I have no idea how to do entire 3a and b i and ii.

 

[zeebujha]

If you are asked to reason why a given data is anomalous :
1. First mention that it doesn't fit the general trend and give a reason supporting that
2. Mention what kind of experimental errors could have resulted in the anomalous result

 

[musa]

Can somebody please help me with this particular question.

October / November 2007, Question 3 A.
I have no idea how to do entire 3a and b i and ii.

well null hypothesis is that there is no effect of grazing on moth population
expected value should be same for all according to our null hypothesis so consider it 114 for all

well for bi degree of fredom is total-1 =2 in this case
bii simply process and check table value as in done in every chi square question

 

[honeycoveredcookie]

Can somebody please help me with this particular question.

October / November 2007, Question 3 A.
I have no idea how to do entire 3a and b i and ii.

well null hypothesis is that there is no effect of grazing on moth population
expected value should be same for all according to our null hypothesis so consider it 114 for all

well for bi degree of fredom is total-1 =2 in this case
bii simply process and check table value as in done in every chi square question

But the mark scheme says that the Expected result is 80 for all. :s
And then they do some weird stuff to the chi square result after that. Divide it by some number :s