Lesson video

Hello, my name's Mrs. Niven and today we'll be looking at how we can analyse some data that's been collected from a solubility practical.

You may have had some experience doing something similar in previous lessons, but what we learn in today's lesson will help us to not only answer that question of how can we explain how substances behave, but it will also provide us with some skills and knowledge that will help us as we move forward in our journey through science.

So by the end of today's lesson, you should be able to draw an appropriate representation of data that's been collected from a solubility practical and also use that representation then to write a conclusion.

Throughout this lesson we'll be using some keywords, which include discrete data, continuous data, bar chart, scatter graph, and line of best fit.

Now the definitions for these keywords are given in sentence form on the next slide.

You may wish to pause the video here so you can read through those sentences or possibly jot them down so you can refer back to them later on in the lesson.

Today's lesson will look at three main things.

Firstly, we'll look at how to decide the best way to represent the data collected in a practical, then we'll look at drawing a useful graph.

And finally, how to write a conclusion.

So let's get started with how to represent collected data.

If you talk to any scientist, they'll likely tell you that the more data you can collect in an investigation, the better your results will be, the better your conclusion will be and the more accurate it will be.

The problem is the more data that you collect, the larger your table of results is, and it's more difficult to identify any patterns that might arise within that data.

So to make it a little bit easier, scientists will take the data collected in a table of results and transfer it into a visual representation, something like a graph.

Using that helps us to identify any patterns or trends in the data.

And from that, we can then finally answer the question we had at the start of the experiment.

Before we take our data and represent it visually, we need to decide the best representation, and that's going to really depend on the type of data that's collected.

So the first type we'll look at here is called discrete data.

Those are measurements that can only take very distinct values or our categories.

Some examples are here such as shoe size, coloured blocks, blood type might be another example.

Usually this type of data is counted as a tally.

So for instance, if we look at the coloured blocks, our table of results might have looked like this.

Continuous data, on the other hand, are measurements that could have any value within a specific range, and because of that, they could be fractions or decimals.

And examples include things like volume, height, or the temperature.

Now specifically or one way to distinguish continuous data from discrete data is the fact that you are using a piece of apparatus in order to collect that data.

So for volume, we'd use a measuring cylinder.

For height you'd use a ruler, and for temperature you'd use a thermometer.

Now, if the data that you've collected in your practical is discrete data, you're going to represent that using a bar chart.

Now, a bar chart shows the data then as solid columns that are individually labelled.

So if we go back to our coloured blocks tally chart and look at the data collected here, the bar chart would look something like this.

If the data that you've collected is continuous data, so remember, that's being collected using some form of apparatus, that data then is going to be represented on a scatter graph.

And a scatter graph represents the collected data as plotted coordinates, something like this for the data that's shown in my table of results on the left.

Let's pause here for a quick check to see how you're getting on.

Which two of the following would be considered continuous data? Take a moment and come back when you're ready to check your answer.

Well done if you said B and C.

Foot length and wingspan are both measurements that would be considered continuous data and you'd probably use a ruler to measure each of those.

Shoe size comes in very discrete units.

For instance, size three, three and a half, four, something like that.

And blood type also comes in discrete, specific categories of things like A, AB, O-, things like that.

So well done if you managed to get those two correct.

Let's try another one.

True or false? A bar chart is used to represent discrete data.

Well done if you said true.

But what's the reason for that? Is it because discrete data is any value within a range or is it because discrete data is a distinct value or category? Well done if you said B.

Discrete data is a distinct value or category, and because of that, that data is going to be represented using a bar chart.

Well done.

Time for the first task in today's lesson.

What I'd like you to do is to use the words provided to complete the summary sentences about what we've just learned about representing collected data.

Pause the video here and come back when you're ready to check your work.

Let's check and see how you got on.

Before I go through the answers in full, I might just take a moment here to recommend that you highlight any words that you've put into and chosen to put into your summary sentences.

For one thing, they tend to highlight some really key ideas or keywords that you'd be using throughout this topic, but also it makes it easier for you to find your answers when you're checking them.

What I would recommend you do is obviously tick them if you get them right, fix them if you got them wrong, and definitely fill them in if you left that blank because you weren't quite sure.

So if you put the words into the correct places for our summary sentences, part A should read like this.

Discrete data are usually a distinct value or a category like blood groups or shoe size.

This type of data are represented using a bar chart.

Part B then should read, if the data are any value within a range, such as 25.

7 degrees centigrade, or 5.

05 centimetres cubed, it is known as continuous data.

This type of data are usually collected by measuring using apparatus and are represented by a scatter graph.

Well done if you managed to get those correct.

Let's move on to the second part of this task.

What I'd like you to do here is to place each example into the correct column for which type of data it might generate.

You may wish to refer to your answers for part one to help you complete this.

Pause the video here and come back when you're ready to check your work.

Let's see how you got on.

So for discrete data, we've already given you the example of car colour, but you could have included then shirt size, number of people, and type of glassware.

For the continuous data, you've already been given the example of handspan, but you should also then include the length of hair, the mass, and foot length.

Well done if you managed to get those correct.

What I'd like you to do for this last part of this task is to match the terms or ideas which are listed on the left hand and right hand sides here to the correct graph which is shown in the centre.

Take a moment here to pause the video and come back when you're ready to check your answers.

Let's see how you got on.

If we start with the top graph in the centre, that is actually an example of a scatter graph and it tends to represent continuous data.

We collect that data using apparatus.

And an example here would be volume of water.

The bottom graph then, the data is usually collected using a tally, and type of sugar is an example of that that you would tally.

It is an example of a bar chart and it's representing discrete data.

Well done if you managed to get all of that correct.

Now that we're feeling a little bit more comfortable deciding how best to represent the data that's been collected in a practical, let's look at how we can draw then a graph that is useful to us.

Before we get started, it's important that you have some pieces of equipment that will help you going forward.

First of all, you definitely need to be using a pencil so that any mistakes that you make can be very easily corrected.

Secondly, you're going to need to be using a ruler so that all the lines that you are drawing are straight and clear.

And finally, you're going to need a piece of graph paper of your choosing.

The first thing you're going to do is to, on your graph paper, use your ruler and your pencil to draw your axes.

Now you're going to want to do this along the outside edge of your paper so that you have lines like this.

And what that does then, it ensures that the largest space available on your graph paper is actually being used to plot the data that you've collected.

The next thing we need to look at is our table of results and remind ourselves that the headings actually list the independent and dependent variables.

And the independent variable is listed first.

The dependent variable is listed second.

These headings then will become the labels on the axes of our graph.

The independent variable heading will go on the X axis, and the dependent variable heading will go then on the Y axis.

So if I put my table of results next to my graph paper, all I'm doing is transferring the heading in its entirety, so that's the name and the unit, down onto the X axis for the label there.

And then for my dependent variable, I'm going to transfer that entire heading including the unit for my Y axis.

Let's take a moment for a quick check.

Which of the following do you not need before drawing a graph of collected results? Take a moment and come back when you're ready to check your answers.

Well done if you chose pen.

We do not need a pen, we need a pencil, because a pen is very difficult to fix.

Tipp-Ex will simply make things messy and it should be avoided at all costs.

Now that we have our axes drawn and labelled, the next thing we need to add are the scales.

And that tells us essentially the value that each box is going to represent on our graph.

When we're drawing the scales, we need to remember that they should be evenly spread and that there's enough space to plot all of the data that's been collected and that those plots will cover at least half the space that you have designated on your graph.

So let's look at how I might draw the scales for this example.

If I look at my independent variable, then for mass I can see that I need to go up to at least 500 grammes.

Now I'm going to try to stick to the larger or the thicker lines on this graph paper that I've chosen and draw some lines there.

And I see that if I put those as representing 100, so that means that each box represents 10 in total, the smaller boxes, and then 10 of the small boxes equals 100, I can fit all of my data along the axis that I've drawn.

As I move to the dependent variable, I see that I need to get almost up to 70.

Now again, if I stick with some of those thicker lines and just draw those in pencil first, I'm going to essentially start by just saying, okay, well, before, I said each box was worth 1 or 10, I'm going to look at the smaller boxes and say, okay, well, maybe they just represent one.

And so five small boxes, my scale goes up in fives.

But when I do that, I only get to 35 as my highest point on my Y axis.

And that's not good enough.

I need to be able to plot all of my data.

So I'm going to rub that out because I've used pencil and instead go back and say that every box is now going to represent two.

So that's the small boxes representing two.

So when I have five of those boxes, it will be 10, 20, 30, 40.

And by that point I can see that I have enough space now on my Y axis to be able to plot all of my data.

Now that my graph paper is fully prepared, I'm going to plot the data from my table of results onto my graph paper like I would for X and Y coordinates, reminding myself so the independent variable is the X axis and the dependent variable is the Y axis.

And when I do that, for instance, for that first set of data at 100 grammes, the extension length was 5.

7.

I'll put a little X.

You could put a dot there, but I think X's are a little bit easier to see later on.

And if I follow that same process, the rest of my data is going to look like this.

Let's pause again for another quick check.

True or false? Scales are drawn using the data values collected.

Well done if you said false.

But what's the reason why? Is it that scales are drawn evenly on each axis, or is it that scales are drawn evenly only on the independent axis? Well done if you said scales are drawn evenly on each axis.

That's absolutely essential so that the data that we are looking at is correctly plotted.

Now that I have transferred all of my data from my table of results onto my graph paper, I need to remember that if I have a scatter graph, I need to include a line of best fit.

Now, a line of best fit is exactly that.

It's simply drawn so that the plots are directly or equally balanced on either side of the line.

And the key here is that it can be straight or it can be curved like it is in this example.

Now why do we care? Well, it's these lines that scientists are going to use to help them to identify any trends or patterns in the data that they've collected.

One thing to remember when you're drawing a line of best fit is this idea of an anomalous result.

Remember, these are ones that do not fit the trend in the data.

It should still be plotted, it should still be transferred from your table of results into your graph paper.

But what you would want to do is maybe circle it to identify it as an anomalous result.

And just like we did in our table of results, we want to ignore this.

So when you're drawing your line of best fit, do not include your anomalous result as you draw it.

Right, one more quick check.

Which result that's been plotted here would you ignore when drawing a line of best fit on this graph? Pause the video and come back when you're ready to check your answer.

Well done if you identified this particular plot.

It is very far away from the rest of them and therefore we would not include it when drawing a line of best fit.

And if I drew my line of best fit, it might look a little bit like this.

Well done on a tricky learning cycle.

Let's move on to the next task in today's lesson.

On the left hand side here, we have the set of data that Sam collected during their practical, and they've created a representation of that data but have asked for you to double check it before it's turned in.

So what I'd like you to do is to circle any errors that you find, and as an extension, you might want to consider how you could fix those.

Pause the video here and come back when you're ready to check your answers.

Okay, let's see how you got on.

There were quite a few things that you could have suggested.

Let's see if you got at least one or two of them.

The first thing that I notice here is that the scale is incorrect on the X axis.

We said they are supposed to be going in regular numbers and instead it looks like Sam has simply taken the values from the table of results and used those as the values that they've put onto the X axis.

The other thing I notice is that the axes are the wrong way around.

Remember we said that whatever came in the first column is meant to go on the X axis, and these have been reversed.

The other thing that you might notice on here is that there's no labels and there's no units that have been included on either of these axes.

So it's really important that the headings from our table of results is also placed on the graph that's being drawn.

I would always recommend that that's one of the very first things that you are drawing on your graph.

Otherwise, the data that's plotted on there will make absolutely no sense to whoever's looking at it.

Well done if you found at least two of those errors.

If you were able to extend your work a little bit and were able to suggest some ways to improve the graph that Sam drew, this is what I would hope you managed to suggest where we have the headings in the correct places on the axes, that the scale is going up in regular numbers and that it should be a bar chart as shown here.

Well done if you managed to suggest any of these improvements.

Okay, let's move on to the second part of this task.

And this is the data that Lucas has managed to collect as he investigated how temperature affects the solubility of powdered sugar when he makes a solution.

Now what you need to do is to consider this data and create a appropriate graph of it.

You want to pause the video here and come back when you're ready to check your work.

Okay, let's see how you got on.

Now, if you've drawn a graph of Lucas' data, it should look something like this.

There are specific things though that I would be looking for if I was marking a graph of someone's data.

And the first thing I'm looking for is that the axes have labels and any units on there.

So essentially taking those headings from our table of results and putting them onto our axes.

The next thing I'm looking for is if you have suitable scales, are they going up in a regular way on each one? They don't have to go up in the same numbers, but they do need to be going up regularly.

So we can see on the X axis they're going up by twenties, and we can see on the Y axis they're going up by fifties.

Next thing, I'm going to be looking that you have correctly plotted the data, so correctly drawn a dot for every set of data that's been collected in your table of results.

And finally, I'd be looking for a suitable line of best fit.

So there were a lot of things to be looking for in this graph.

The main thing I'd be starting with is have you got the labels for your axes in the correct places? And do your scales go up in a regular formation? The plotting and the line of best fit will come with practise, but as a start, you should be getting those labels and scales correct.

Well done.

Now that we've managed to take our collected data and transfer it onto a useful graph, let's look at how we can use it to write a conclusion.

So just like what you may have learned in English, conclusions in science summarise things, it summarises what you found out in your practical investigation.

And a really good rule of thumb to use when trying to write a conclusion is to simply say what you see on your graph for any trends or patterns that are found.

So if I look at this bar chart here, I can see that the sugar cubes have the highest bar, and therefore based on the label for the Y axis, it took the longest time to dissolve, whereas the powdered sugar has the smallest bar, which means it took the shortest amount of time to dissolve.

And that's simply the conclusion I could write for this particular bar chart and set of data that was collected in my practical.

Now, the difference between an okay conclusion and a really good one is that the better one is going to refer to both the independent and dependent variables as well as describing any relationship we see between them.

So a really good rule of thumb for a sentence you could write is, as the independent variable increases, the dependent variable does what? And that's where you'd refer to your graph.

Now if you're not sure what the independent variable is, remember that we have that listed in our table of results and transferred that data, including its heading, onto our graph.

So the independent variable is actually just the X axis label.

So I could say for this particular graph, as the mass increases, I then need to consider how the dependent variable changes, and that's when I'm going to look at my line of best fit.

So the dependent variable is simply the label for the Y axis.

And if I look at that, I can see that the line of best fit is going up as my mass is changing.

So my conclusion will say, as the mass increases, then the extension length is also increasing, because the line for the extension length, those plots are going up as well.

So the difference between a good conclusion and an outstanding conclusion is that you're also identifying any changes that you might see within this pattern.

So we already said that as the mass increases, the extension length is increasing.

But what I can see is that as you get past 300 grammes of the mass, that line really starts to slope upwards a lot faster than it did at the beginning of that investigation when the mass was quite low.

So I could add an extra sentence to my conclusion and simply say that the increase in extension length becomes greater over 300 grammes.

And that just adds a little bit more detail to your conclusion, making it even better than it already was.

Let's take all of that information now together to write a conclusion for the graph that's been provided.

So looking at this and remembering my guide of, as one variable changes, the other does this, my conclusion for this particular graph would read: as the mass of sodium nitrate increases, the change in temperature increases, it's also going up.

Now I can't see any major change in that increasing nature, so I'm going to leave it at that.

What I'd like you to do now is to take a moment and try to write a conclusion for this graph using the information and guidance that you've seen so far in this learning cycle.

So pause the video here and come back when you're ready to check your answer.

Let's see how you got on.

If you followed that guidance, your conclusion may have read something like, as the sunscreen SPF increases, the time for a UV bead to turn purple increases as well.

You may have said that it starts to increase a little bit faster after 15 or 20 SPF, but that isn't absolutely necessary here because it's quite tricky to see that specific guidance.

But as long as you have, as the SPF increases, the time for the UV bead to change colour also increases, you're on the right track, so well done.

Let's pause a moment for another quick check.

Which two of the following should always be included when writing a conclusion to an investigation? Well done if you said the independent variable and the dependent variable, A and C.

A control variable doesn't change and so it's very rarely included in a conclusion.

Well done.

Let's move on to the last task for today's lesson.

So for the first one, what I'd like you to do is draw a conclusion for this graph.

Pause the video and come back when you're ready to check your work.

Let's see how you got on.

So for this graph, a good conclusion would read something along the lines of, as the mass of magnesium increases, the volume of hydrogen produced increases.

An even better conclusion would also include the change that we can see here by saying at about 0.

60 grammes of magnesium, the volume of hydrogen produced stays constant or no longer changes.

You could have also included something along the lines of, once the volume gets to about 600 centimetres cubed, the volume does not change any further.

Something along the lines that we now have a straight line at that particular volume to show that it's no longer changing.

Well done if you got the first sentence, and extra bonus point if you got the second sentence.

Let's try another one now.

I'd like you to write a conclusion for this graph.

Pause the video and come back when you're ready to check your work.

Let's see how you got on with this one then.

A good conclusion would say something along the lines as, as the time increases, the mass decreases, but we can see as well that there is a change in this line of best fit.

And so an even better conclusion would include an extra sentence along the lines of, at about 70 seconds, the mass remains constant or no longer changes.

So again, well done if you got the first sentence, and an extra bonus point if you got the second sentence as well.

Good job, guys.

Well done.

For the last part of this task, I'd like you to write a conclusion for the graph that was drawn in task B part 2.

If you weren't quite pleased with your graph that you drew there, you can use this one as a reference and write a conclusion for that.

So pause the video here and come back when you're ready to check your answer.

Okay, let's see how you got on.

So if you wrote a conclusion for this particular graph of data, you should have had a sentence somewhere along the lines of, as the temperature of water increases, the time taken to dissolve decreases.

Now on this particular graph, we can't see a change in this line of best fit, and because of that, there is not an additional sentence that you could write unless you wanted to include specific reference to a data point.

But at this stage, this is a perfectly reasonable and good conclusion for this graph.

Well done, guys.

You've been absolutely smashing today's lesson.

Okay, let's summarise what we've learned in today's lesson.

Firstly, we've learned that scientists will create a visual representation, which is usually a graph, of the data they've collected during a practical so that they can find any trends in the data that's been collected.

And the type of representation that is used depends on the type of data that's been collected.

So discrete data will be shown in a bar chart, whilst continuous data will be shown using a scatter graph.

We also learned that lines of best fit would be added to a scatter graph and that they can be either straight or curved.

And it's these lines of best fit that describe the relationships between the variables if there is one.

And finally, we've learned that a conclusion summarises what was found out in this practical, essentially answering the question of that experiment.

And it always refers to both the independent and dependent variables in that sentence.

You guys have been absolutely fantastic today.

I hope you've had a good time learning with me and to see you again soon.

Well done.