hi guys and welcome to this video on answering database questions my name is Joakim I'm an XIV student that scored 43

points overall with sevens in higher level biology chemistry and economics and I work at the company I'd be better

which provides online provision courses for IB subjects so where you can revise essentially everything you need to know

for your exams in just a few days you can check that out at www.weather.gov/elko so let's just talk

about the structure of these database questions first of all so the first thing you've probably already know is

that they are the first part of paper two and they comprise roughly 20% of the marks overall for the paper - again and

essentially the way that it's structured is that the Ibo provides you with some kind of background about an experiment

and then shows your graph and then shows you some more text and then shows you another graph and then asks you some

questions about it now why are the Ibo asking you these kinds of questions why is it even in

there well it's because they're training you to become real scientists okay and if you think about it what a scientist

actually does is not spend most of their day just memorizing textbooks and then regurgitating those on an exam a few

months down the line now instead what they're actually doing is that they're conducting experiments usually right so

some kind of investigation which involves setting up a hypothesis doing an experiment getting some data and then

somehow working out what that data shows and so the Ibo is trying to train you to become scientists by forcing you to

interpret data and to evaluate how data relates to conclusions and hypotheses now although that might sound a little

bit complex it's actually not as difficult as it's made up to be because there are largely only five different

types of questions that the Ibo asks you regarding different types of data and there's ways you can prepare for those

types of questions which is exactly what we're going to do in this video so the five most common

types of questions that get asked are those where you're asked to state some kind of a value from a graph and one

where you're asked to calculate something in a graph and outlining or describing graphs or

friends or relationships and comparing results from different types of data or different trials in an experiment and

then finally evaluating conclusions and sometimes hypotheses as well so these are the five different types of

questions that we're going to go through in this video and I'm going to show you exactly what you need to put down for

each of these types of questions and just systems when thinking about these questions okay the first question we're

gonna have a look at is the state question which is typically what kicks off these data base questions so after

you've given been given an initial passage and then a graph you'll then be asked to state the and then usually it's

going to be the maximum the minimum of the general trend okay so this is a really straightforward type of question

that is worth one mark and so let's just have an example of what one of those questions typically looks like okay so

here's a pretty classic example in this case we're shown a graph in which you have the total carbohydrate protein and

chlorophyll content in some kind of a cell we don't even need to look at the passage you don't need that for these

types of questions and then on the x-axis we have these different groups to the control group DDM a gdp and a p okay

so those are abiotic stress more parameters apparently we don't even need to know what that is in order to answer

these questions so all it says here state the highest carbohydrate achieved by any group in the graph above for one

point okay so essentially the only thing they're testing here is your ability to interpret the graph so what you have to

realize the answer this question is that the carbohydrate level is marketed or marked by the dark green line right

based on that key down in the bottom and so in order to find the total lipid or carbohydrate content essentially you're

just gonna have a look at where is that green bar the highest and you can see that it's in the control group and it's

somewhere in between 150 and 200 almost slap-bang in the middle and so your answer is going to be 175 now the only

thing to get to make sure of when you're answering these questions is to make sure that you're using the units as well

okay so it's gonna be milligrams per gram dry sell weight so the answer here would be 175 milligrams per gram so a

pretty straightforward question this probably shouldn't catch you out as long as you're just taking your time

and looking at the graph carefully okay the second type of question that gets asked is usually some kind of

calculation okay and this can sometimes confuse people but it really shouldn't because these questions are either going

to be about adding or subtracting values or it's gonna be about calculating percentage differences or it's about

calculating a mean or an average okay so these aren't complex differential equations or anything like that it's

very simple mathematical formulas but the one thing that I think does confuse people sometimes is about calculating

the percentage difference so we're just gonna go through how to do that so in this example we're asked to calculate

the percentage increase in glucose concentration from fasting to postprandial mice okay so this

experiment must have been involving mice right because that's haven't even been given here and we can see that on the

y-axis there's this glucose concentration and on the x-axis we have these different time periods it seems

and so we're asked to calculate the percentage increase in glucose concentration so some value over here

from fasting to postprandial Merritt's okay so it says fasting here postprandial so that must be what that's

talking about so we essentially have to calculate what is the percentage difference when we go from 214 to 262 so

what we mean by percentage difference is what is the difference between the first bar and the second bar expressed as a

percentage of the original okay that's really important it has to be over the original that's the formula for this so

in this case we're gonna say the way to calculate this is to take the new values of 262 minus the old value okay so find

that difference and then express that as a fraction of the original one so new minus old 262 minus 214 is gonna be 48

48 divided by the original value of 214 is 22 point four percentage increase okay so that's going to be the value

there so it's always the always is going to be about calculating the difference and expressing that as a fraction of the

original and then converting that into a percentage okay the third type of question get Beckett's ass

for the database section is some kind of an outlining or describe question for the graph okay so it's typically going

to be outlining the trend that you can see in the graph or outlining the effect of one variable or another okay and when

you're asked to describe a graph the points that you are typically given away are first of all for the general trend

so here we're talking is that a positive correlation is there a negative correlation or is there perhaps no trend

at all there's one thing cause another thing to increase this one thing cause another thing to decrease okay so just

think general here what's like the most obvious thing you can see and then you're then probably also going to get

points for some kind of an important outlier okay so is there one piece of data that is clearly very different from

the rest of the graph and then the final thing you need to talk about is is there an important maximum value or minimum

value okay so if there's a curve that goes up and then down it might be important to mention okay well there's a

maximum peak at this point here and it has a value of this okay so this is typically will be where two or three

marks so there's a few things that you'll need to pick out for these graphs okay now let's have a look at the

practice question so for this question we're asked to describe the change in mean mass for the female rats during the

90-day experiment worth two marks just to give you a little bit of context what it would have said in the paragraph

above this graph is that they were essentially testing how you could feed mice with different types of rice rats

in this case with different types of food and then track their mass change over time okay and so when we're asked

to describe grass there's a few things that typically you'll be able to mention about most graphs okay so the first

thing you want to ask yourself is is this a positive negative or no correlation okay so is are both

variables increasing over time or is one decreasing as the other increases or is there perhaps no effect at all okay so

in this graph it's the first obvious thing is that as the test days increase all three of these lines those are going

to be increasing in mass right so that's the first thing that's perhaps the most obvious point so that we already have

one of those two given away okay well what else can we say about this graph well you can have a thing for yourself

but one thing that I've noticed in looking at it is that it seems like this curve kind of flattens out over time

right if you were to look at this this line it's not going to be a straight line so the slope of the initial few

days isn't the same as towards the end right because here it's almost flat you know all the lines whereas initially

there's a much steeper increase so that's unciekin point that I might say which I'm quite confident about which is

that the initial gain mass over the first that's perhaps let's say like four to two days right is more steep or is

greater than in the second half of the experiment so that would be my second point and I'm pretty confident about

both of those but just for safety let's just add in another point since this is worth two marks so what else can we say

well another noticeable difference I would say in this graph is that you see this top line here which represents rat

food that one is clearly separated from the other two points right and I feel like that's quite a relevant finding for

this experiment because if these two are together and this one is different then that's that's something important right

there's something to take note of so a third point that I might say to try and pick up some points is that the rat food

showed the greatest increase in body mass consistently over the entire experiment right you can see that even

from the very beginning the rat food is always above the other types of feed and in this case okay so I feel like those

three points are definitely valid points which someone would find important for this experiment right so that's kind of

the questions you have to ask yourself when you're describing or outlining graphs is what are the most important

features that someone would want to pick up on if they're interpreting this graph the fourth type of question we're gonna

look at are the compare and contrast questions and here what you're asked to do is find similarities and differences

between different data groups okay now when you're asked to compare and contrast either graphs or effects or

something like that a really nice way to answer these questions is to look at similarities first and then differences

afterwards okay so if you have to look at both of them then split up your answer first look for things that are

similar between values and then look at differences okay and then make sure that you're identifying the exact variables

that you asked to compare right so are you being asked to compare the absolute glucose

concentration in two different mice for example or are you asked to compare the change in glucose concentration so the

slope of a line in two different mice right so just make sure that you know exactly what you're comparing and then

when you're giving these descriptions of comparisons or contrast make sure that you make it super obvious okay so use

words like in the one graph there is a positive correlation whereas in the other graph there's a negative

correlation right so again you're illustrating the fact that you know that that is a difference okay so you're not

leaving it up to chance for the examiner to to work this out okay now let's have a look at a practice question so in this

question what we're doing is we're comparing the results between the plants treated with short daylight and the

plants treated with long daylight so in the paragraph above this experiment what it would have said is that they were

testing different types of flowers for the response to different types of light depending on what genes they had so they

have some different flowers with different genes and then they looked at what would happen to their flowering

time when they treated them with different types of light and so we're asked to compare the results of those

treated with short daylight and long daylight okay whenever you're going through these types of questions of

comparing it's important that you both give similarities between the two graphs as well as differences so when you're

giving these questions I think a good system is to split your answer so first talk about things that are similar and

then talk about differences so what can we say is similar between these two different types of exposure well what we

can see while were what I can initially see is that in both cases for short daylight and long daylight it seems like

the white bar was always the lowest value right so the white is lower than all three of these other bars in both

cases so that's one thing I would say is similar between the two what else is similar well in both cases we could also

say that this very dark line is the very is the one that took the longest time okay so those that are homozygous for

the overexpress OSGi team took the longest time to flower in both instances right

that's probably the most obvious thing so I'd probably put those two down for comparison's and then when we're doing

contrasting so when we're looking at differences well what can we say okay well let's have a look at what's

different about the responses in these flowers compared to these flowers well the first thing that I noticed when

looking at these two is that when these plants were treated with long daylight these first two bars those increase

right so those go from from being down here to being up here so that's one thing but on the other hand these two

bars those decrease over time so I feel like that's quite a noticeable finding so what I would say then is that in the

short day light and compared to long daylight long daylight increase the time until flowering in specifically these

two types of flowers so in the wild type or the WT and in those that were not over expressed for the OSGi gene okay so

that's a noticeable finding whereas on the other hand the the long daylight caused the shortening in the flowering

time for those plants that were heterozygous for the overexpress OSGi gene and homozygous for the OSGi gene

okay so again I feel like these are noticeable findings and so you want to try and pick these out out of the graph

okay and since this is two points and we've already given four points at this point and that's a lot of points I feel

like that's an appropriate amount to give for that answer okay so the it's the key thing here is to be systematic

first look for things that are similar and then look for things that are different don't try to do the entire

graph at the same time just look for key features that change over the graph okay the final type of question we're going

to discuss are the evaluate or discussed questions so these are usually phrased something like this based on the graph

or the data above evaluate if whether the conclusion bla bla bla is supported it could also be the hypothesis okay

so usually this involves looking at multiple gratz or multiple pieces of data that you might already have

interpreted in the previous answer so this is typically one of the last questions for for the data analysis

section and when you're asked to evaluate a question a lot of students they tend to climb up because they're

like okay well what exactly does it mean to evaluate a conclusion how do you even do that

essentially what you need to do is you need to have a look at the conclusion or the hypothesis or whatever they're

asking you to consider and then find arguments for and against that conclusion or hypothesis in the data a

case and there's always two sides so if you can't find something that both support and refutes it you probably

haven't looked hard enough okay there's always gonna be something that goes against it and with it so the key thing

here is to essentially look for something in one of the data that might support the conclusion and then

something that might go against it and it's important that you quote the data okay so give exact references so in

graph a or whatever if there's a positive trend from from x 0 to x 7 okay so this is that's like key data and then

the final thing you need to do is give a given overarching opinion so that is based on all the data that you found

overall do you think the conclusion or about our hypothesis should be supported or refuted and this part is hard to

actually teach because essentially it's your instincts about okay well which data is most important what does most of

the data say about this hypothesis and so you then have to say overall based on all the data I think the conclusion is

supported or refuted essentially let's just have a look at a very simple example so usually you're gonna be asked

to look at different types of graphs but I just want to illustrate the kind of thinking that you need to go about and

and when answering these questions so it says here evaluate the argument that increases in co2 concentrations can be

attributed mainly to seasonal variations okay so in this case what we're looking at is famous graph by the mama lure

Observatory in Hawaii which shows that over the last 50 years or so the co2 concentrations have been increasing okay

so even though from an initial standpoint if you first look at this data right is the main reason why this

day why this graph is increasing is that because there's a seasonal change well no of course not right because we're

talking about many different years so initially you might think okay well I don't even know how I can argue both for

and against this this statement because it seems so obvious that this is obviously not just due to a seasonal

change but then you have to remember that you have to argue both sides so what could we find in this data there

might actually be some sort of support to this argument that this increase in co2 is due to seasonal

variations well one thing you might notice is that within every year and you've also talked about this in the

actual IP syllabus there are both increases there's this constant increase and then dip again right there's these

spikes dip spike dips and it looks like this is every year right so essentially there's a period of of high co2 in the

in the year and then a period of low co2 and that is definitely due to seasonal variations right so if you remember in

the summer there's gonna be a lot more photosynthesis happening which is when the co2 is going to get sucked out of

the air whereas in the winter there's less photosynthesis happening and so you have this increase in co2 right because

there's less absorption of co2 so that when you're arguing for this experiment what you could say is well something

that might support the conclusion is that there is some variation between within every year because of the seasons

and but then when you go against the conclusion you might say okay well even between the same season each year so in

between these dips you see that there's an increase over time okay so the overarching conclusion is that and

overall you cannot support this argument based on this data right because they're over arching the reason for this

increase is probably not going to be just a seasonal change and so that's the type of answers that they can be looking

at now usually you're going to be looking at much more complicated graphs and you're going to be looking at

multiple graphs and but the key thing to remember is just think of things arguments for think of arguments against

and then synthesize a final opinion at the end and that way to get good at this is simply just by practicing looking at

loads of data okay okay so that was the end of the five most common types of questions that get

asked in the data analysis section of the paper - for biology HL so you might be asking yourself okay well why or do I

go from here so I feel like I'm not actually at the point where I can answer or sit an exam on these types of

questions yet and the thing I would say up to that is practice practice practice okay so the only way to get good at data

analysis questions is to simply just have a look at loads so I would aim to at least go through 20 different

questions before your final exams and just practice answering the questions and then looking at the markings and

what type of things are they picking out of the graph okay and you'll quickly realize that there's actually some

really common trends then you'll get really good at answering those questions so that's what I'd highly recommend and

then finally if you are looking to revise for your final exams don't forget that we also have online revision

courses for biology HL and SLE where we cover the entire syllabus including data analysis questions and essentially walk

you through exactly everything that you need to know in preparation for the final exams so check that out if you

feel like you might need it otherwise thanks a lot for watching and see you next time