Lesson video

Hello, my name is Chloe and I'm a Geography Field Studies Tutor.

This lesson is called "Field Work: Analysing, Concluding, and Evaluating Soil Data." It forms part of the "Rocks, Weathering and Soil: Why is Geology Important?" unit of work.

In this lesson, we're going to be looking at the kind of data that we collected during our field work, and then thinking about what it all actually means.

Is there a connection between the different elements that we studied? And what does this all mean in terms of the wider picture of soil texture? Let's get going.

By the end of this lesson, you will be able to analyse and reflect on your soil data to create meaningful conclusions and an evaluation.

There's three key words to think, consider first of all.

First of all, anomaly.

This is a piece of data that does not seem to fit the trend shown by all the other pieces of data.

Valid data is data that is able to answer or partially answer an inquiry question.

Reliable data is data that could be replicated if the data collection were to be repeated.

There are three parts in this lesson.

We're first going to be analysing some soil data.

We're then going to be writing a conclusion.

And finally, we will evaluate our entire geographical inquiry.

But let's start first of all with that idea of analysing the soil data.

So, we're onto this stage of our enquiry cycle, data analysis.

Geographers often start their analysis by making some simple descriptions such as commenting on the most or least of something.

So, here's Sam.

Sam says, "Site 1 had the most amount of clay and the highest level of compaction." She's looked at the fact that that hammer in our diagram is really large at site 1, but also the amount of clay represented by the green shading in the waffle chart is also relatively large as well.

Geographers also comment on any relationship they might see between the sets, such as a consensus or an agreement.

Jun recognises that the sites with the slowest infiltration times tended to have the least amount of sand.

They will also comment in any relationship they see where there's conflict or disagreement.

So, Andeep notices that at site 1 and 2, these both had relatively small amounts of silt, but very different compaction scores and infiltration times.

Let's take a look at that.

So in both cases, blue colour that represents the silt is relatively small in the waffle charts.

But if you look at the size of the hammer between 1 and 2, they're completely different.

And likewise, the infiltration rates are very different as well.

So, clearly there's some other factor going on there.

So, let's check our understanding so far.

True or false? In the diagram that you can see here, the site with the most sandy soil had the fastest infiltration rate.

Pause the video so you can have a really close look at those diagrams and then come back to me and tell me if it's true or false.

Well done if you see that it's true.

Yes.

So, the site with the most sandy soil is gonna be the site where the waffle chart has the most of the shade purple in it.

That is site 2.

And if we also look at site 2, it is the one with the fastest infiltration rate.

On the stopwatch there, the pink segment is the smallest out of the four sites, so we know that that statement is a true one.

Once geographers have described their data, they then try to explain it.

And there can be lots of reasons why the data appears as it does.

In this example, our explanations are based on how the structure of different soils creates different sized particles and different pore sizes.

Izzy is using this theory to explain some data.

She's looking particularly at site #2.

She says, "Site 2 has the highest amount of sand in the soil and the fastest infiltration rate.

This could be because sand particles are large and that creates large pore spaces.

This means that water can pass through the soil quite quickly." Izzy's done a really good job there of using geographical theory to explain her data.

One should also look carefully at data that does not fit the expected pattern or theory.

Let's look at site 1 and 3 here with Laura.

She says, "Sites 1 and 3 have really similar soil structures but totally different levels of compaction.

Site 1 is far more compact than site 3, but the infiltration rate at site 3 is far slower.

It doesn't make sense!" Let's take a look at the data just before we move on.

So, she's recognising, yes, they have fairly similar soil structures.

Our waffle charts in both of those diagrams, there's a little bit of variation, but they are pretty similar.

But yes, the compaction rates are completely different.

Site 1, very large hammer representing a large number of strikes on the soil auger and that meant that the soil was really compact there.

However, at site 3, really small hammer means it barely took any effort to put that soil auger into the soil.

Then, she looked at the infiltration rates, and they are completely different as well.

Site 3 took a long time for the water to infiltrate, nearly a whole minute compared to site 1 where it's about 42, 43 seconds.

What we would expect according to the theory, is that Site 1 would be really slow in terms of infiltration.

It's a really compact soil.

That would mean that the particles are very close together.

The pore sizes are very small, if in existence at all.

It means that the water would really struggle to get through the soil because it's so compact.

But actually it's the other way round.

The soil at site 3 doesn't seem to be very compact at all.

It took no effort to get the auger into the soil.

But it did take a long time for the water to infiltrate.

So, something doesn't make sense here.

Laura's right.

This leaves Laura kind of thinking, "Did I make a mistake in the data collection?" Geographers try to find reasons for anomalies in their data.

Only if they can find no geographical reasons for the data being different, will they say that the difference may be due to human error.

In other words, they'll try to find any geographical reason for their data before they fall back on the idea that they did something wrong in the data collection.

In fact, human error in the data collection does, I mean, it does happen, but it's actually the least likely reason why your data would be different to what you would expect.

Let's look at the conversation now between Lucas and Laura.

Lucas says, "Don't worry.

Let's look at the main differences between the sites to see if we can find a reason." You can see that Laura here is thinking that she's done something wrong in the data collection to mean that there's an anomaly in her data.

Lucas is looking at it the other way around.

He says, "Let's try and find a geographical reason why the data is different." So, they're gonna look at the actual sites to see if there's a reason.

Here's the map and it shows site 1 and site 3.

Laura says, "Both sites are on the playing field, so that can't be a reason for the difference in the infiltration rates." Lucas says, "But site 3 is downhill of site 1.

All the rainwater runs down to that side of the playing field.

It could be that site 3 was already partly saturated with water.

This would have slowed down the rate of infiltration." So, although above the surface, site 1 and site 3 look to be very similar, if site 3 is downhill, it's likely that there's already quite a bit of water in the soil there.

So, there's not gonna be anywhere for the water to go that Laura is pouring in in her infiltration survey.

The pores are already full of water effectively.

That would have definitely affected her results.

So, let's check our understanding there.

How should a geographer deal with an anomaly in their data? A, ignore it, B, try to find a geographical reason for it, C, assume it is due to human error in the data collection, or D, recollect that particular piece of data.

Pause the video and have a think, and then come back to me.

Well done if you got answer B.

Yes, you would always try to find a geographical reason for it first.

Only when you've explored all ideas around geographical reasons for your data being as it is, might you then say, "Well, it could be due to human error." Here's our first practise task for today's lesson.

Look at the soil survey data that you collected on your own school site.

Describe and explain the data using simple statements.

Then, try to identify an anomaly or an aspect of the data that you were not expecting.

Suggest a geographical reason for the difference in the data.

You will want to pause the video here, have a really close look at your data to try to get those two tasks done.

Come back to me and I'll show you some of my ideas.

For the first part of that task, you needed to describe and explain your data.

Here's something that your answer may include.

Site 2 has the highest amount of sand in the soil and the fastest infiltration rate.

This could be because sand particles are large and create large pore spaces.

This means that water can pass through the soil quite quickly.

So, my paragraph starts off with a simple description.

I then try to explain it and I bring in the geographical theory that it is based on.

I then want to try and identify an anomaly or an aspect of the data that I wasn't expecting.

Here's an example of the kind of thing that your answer could include.

Sites 1 and 3 have really similar soil structures, but totally different levels of compaction.

Site 1 is far more compact than site 3, but the infiltration rate at site 3 is far slower.

So, that's my identification of the anomaly.

Now, I need to try and explain it.

This could be because site 3 is downhill from site 1.

This means rainwater runs down to that side of the playing field.

Site 3 may have already been saturated with water compared to site 1.

This would have slowed down the rate of infiltration.

Do note the type of language I've used there.

I've said that site 3 may have already been saturated.

I didn't test for that, so I don't know for sure.

I'm suggesting that this is a reason.

I'm not telling you that this is the absolute reason why the data is as it is.

Do check your own language that you've used.

Now, we're moving on to the second part of the lesson.

We're going to look at how we might conclude this soil data.

So, here we are in the enquiry cycle.

We've moved on to the conclusion.

Geographers begin their conclusion by reviewing the main points of their analysis.

They need to decide which of their observations are the most important and have the strongest evidence to support them.

Geographers can then answer their enquiry question.

Let's remind ourselves of the enquiry question that we are dealing with.

We are looking at how does soil texture affect infiltration around our school site? Alex is reviewing his analysis and the strength of his evidence, and he makes some notes on this.

Let's take a look at the clipboard to see what notes he makes.

So, the strong evidence that he finds is that sites with greater amounts of sand in their soil have faster infiltration rates.

He also notices that soil types seems to have a stronger influence on infiltration than soil compaction.

The weaker evidence is that the position of the site within the school grounds has an impact on the rate of infiltration.

There is some evidence of this, but it's not true for all of the sites.

By drawing on the data that produces the strongest evidence, Alex is now ready to write his conclusion.

Let's check our understanding about conclusions.

Complete the sentences with the missing words.

Pause the video so you can have a scan through the paragraph and then come back to me with some suitable words that can fill in those gaps.

Let's look at the ideas that you've had.

Geographers begin their conclusion by reviewing the main points of their analysis.

They need to decide which of their observations are most important and have the strongest evidence to support them.

They can then answer their enquiry question.

Hope you managed to get those words in there.

The conclusion then addresses any hypotheses made at the start of the enquiry.

Aisha's hypothesis was this.

She said, "I hypothesise that the soil samples with the highest percentage of sand will have the fastest infiltration rates." Aisha's hypothesis can be accepted.

In fact, it can be really strongly accepted as the fastest infiltration rate occurred at the site with the highest amounts of sand in the soil.

Jacob's hypothesis was this.

He said, "I hypothesise that the areas with the greatest soil compaction will have the slowest infiltration rates." Jacob's hypothesis can be partially accepted.

The site with the most compact soil has one of the slowest infiltration rates in our example, but it wasn't the slowest, so it's only a partial acceptance of his hypothesis.

It is rare that geographical theory that geographers learn about matches the real world conditions experienced during field work.

This is because there are a lot of variables acting upon the data in the real world that geographers often do not explore or don't explore in detail in their theory.

And these variables will change from place to place.

Aisha says, "I'm pleased that my hypothesis was shown to be correct from the evidence I collected during the field work.

But what do I now write about in my conclusion?" It seems strange, maybe to be a little bit disappointed that your hypothesis is so cleanly correct.

And she's kind of right.

Sometimes if the results are perfect, it means it can be really difficult to find anything to discuss or anything interesting to say in the conclusion.

She says, "Having unexpected results could make my conclusion a bit more thought-provoking." Isn't it strange to sometimes hope that your data is actually a little bit different from what you were expecting? It means that you would have something more to discuss.

So, what do you do if your results are perfect? Aisha's conclusions might discuss things like this.

The extent to which her results confirmed her hypothesis.

Whether her results were as strong as she expected.

The particular conditions that made her results so strong.

Rather than just saying, "Yes, my hypothesis is correct," actually go into more detail and think about, "Well, what was it that made my hypothesis so strong, so correct?" or "Was it actually that there was maybe another factor involved as well that made it even more correct than I was expecting?" Let's check our understanding of that idea.

What might geographers discuss when their evidence matches their hypothesis exactly? A, what went wrong in their data collection, B, how other variables influence the results, C, whether the results are as strong as expected, or D, whether the results were highly conclusive or only just conclusive.

I'll give you a clue here.

There might be more than one correct answer.

Pause the video and have a think about Aisha's predicament and then come back to me.

So, it is certainly true that yes, you would hope that that person would be able to talk about whether the results were as strong as they expected, and if your results are perfectly aligned with your hypothesis.

You should also be talking about whether they were highly conclusive or only just conclusive.

Let's move on to our second practise task of today's lesson.

Write a conclusion in relation to your own data analysis.

State whether any hypotheses you made are accepted, partially accepted, or rejected.

So, go back over your analysis and have a think about those hypotheses you made to see what you can now say in relation to your data.

Pause the video and come back to me with your ideas later.

So, let's look at the kind of things you should have included in your answer.

There should be a statement that answers your enquiry question.

There should be a summary of the strongest evidence.

And there should be a statement that says whether your hypotheses are accepted, partially accepted or rejected.

And importantly, you need to say why.

Now, we'll move on to the final part of this lesson where we're going to be evaluating our enquiry.

Here we are in the final stage.

In every field work enquiry, there will be things that go well and as expected and things that do not go to plan.

Sofia and Andeep are reflecting on their fieldwork enquiry.

Sofia says, "There was a lot to do at each site, but the equipment was actually easy to use.

In our groups, we could divide the tasks up so that we all had a role to play." Andeep says, "I'm not sure the compaction test was very accurate though.

I was responsible for hammering in the soil auger, and I can't be sure that I used the same force every time I struck it with the mallet." Think about it.

That's actually a really hard thing to expect of somebody.

"This meant the results might not have been comparable." Good point.

Being a good geographer means being honest about the shortfalls in a field enquiry.

What is most important is that you say what you would change to make the enquiry even better.

Sofia says, "If I were to do the enquiry again, I would choose a less complicated data presentation method.

It took me a long time to draw all the elements accurately, and by the time I got to site 4, it all felt a bit rushed." Mm, I can imagine.

Yes, you more complicated your data presentation, you might kind of lose the motivation by the time you get to the final site that you are presenting.

Let's check our understanding there.

Why is it important to keep the methods of data collection consistent from site to site? To ensure the results are comparable from site to site, to ensure that every member of the group has an opportunity to learn, or to ensure that the results are the same.

Pause the video.

Hopefully they're not too difficult, and then come back to me with the right answer.

Hopefully, you all got that one right.

Yes, it's A, to ensure the results are comparable from site to site.

Geographers also reflect on the data itself.

They ask questions like, "Was the data valid? Was the data reliable?" These questions ask geographers to think more deeply about their data and the nature of it.

Sam says, "Well, how would I know if my data is valid and reliable?" Good question.

To evaluate the extent to which data is valid, geographers think about what data was needed in order to answer their enquiry question.

Now, our question was this.

"How does soil texture affect infiltration around our school site?" The data we needed was the soil types that are found in the school grounds.

We wanted to know how compact the soil is at different sites around the school grounds.

And we wanted to think about how quickly water infiltrates through different soil types and through soils at different levels of compaction.

We collected data on those three things, so we can say that our data was valid.

To evaluate the extent to which data is reliable, geographers consider factors that they can and they cannot control during their data collection.

So, things that geographers generally can control, they can definitely control the amount of water that they use in infiltration survey, and they can make sure that it's the same amount of water at every site.

Pretty much they should be able to control the accuracy of reading the ruler against the measuring cylinder.

They need to just take their time, and that is a controllable factor.

They can also take control over where they actually do the data collection.

The locations of the different soil texture surveys was in the control of the geographer.

But there's also factors that they cannot control.

So, it's really difficult to time that water flowing through the soil with a stopwatch.

It might take a little bit of practise to actually get that accurate and to stop the timer on exactly the right moment.

The preceding weather conditions will definitely have had an influence over the soil.

If it had rained the day before the surveys, the soil might have already been saturated with water.

Or if there'd been a period of drought, it would've also had an impact.

It would've meant that maybe the soil would've been cracked and more compact than you might expect.

Another factor and something which was brought up earlier is that you can't necessarily control the force at which you're hammering the auger into the ground.

Sometimes you might hit it very softly.

Sometimes it might be a bit harder.

So therefore, yeah, that could be a variable that would affect the reliability of the data.

If you can control most aspects of the data collection, the data is likely to be reliable.

So, let's check our understanding there.

True or false? To have reliable data, geographers want to have more uncontrollable than controllable variables in their data collection.

Is that true or false? Have a think about the examples we just had.

Pause the video and then come back to me.

Yes, well, hopefully you should see that that is a false statement.

How would you change that statement? Or why would that statement be considered false? Geographers want to have as many controllable variables as possible in their data collection to ensure that their data is reliable.

We now move on to the final practise task of this lesson.

I'd like you to write an evaluation based on your soil texture fieldwork enquiry.

Include the following points in it.

A discussion about something that went well, a discussion about something that you would change and why you would change it, and a discussion about the extent to which the data is valid and reliable.

You might want to have a chat with somebody nearby to see what they came up with in their surveys.

Were there problems that they experienced, which you didn't that you might want to discuss as well? Pause the video and then come back to me with your ideas.

Here's some ideas that you might have in your evaluation.

The equipment was easy to use, and in our group, we could divide the tasks up so that we all had a role to play.

If I were to do the enquiry again, I would choose a different data presentation method.

The one I chose meant that you had to always refer to the key to understand what the data was trying to show.

Remember, you're evaluating every aspect of your enquiry, not just data collection.

The data was valid as the three elements of the data, soil compaction, soil type, and infiltration rate all combined together to tell us how these different variables influenced each other.

The data may not have been entirely reliable as there were a lot of variables that we could not control.

For example, we had no control over how the weather conditions over the proceeding days might have affected the data.

Let's now summarise our learning from today.

Geographers analyse their data by describing and explaining it.

This includes looking for relationships between the datasets.

Geographers always try to find a reason for data that appears to be an anomaly before assuming it is a result of human error.

In their evaluation, geographers consider if their data is both valid and reliable.

Well done.

There's a lot to think about in the analysis, the conclusion, and the evaluation of a fieldwork enquiry.

Maybe it's just me, but I kind of like it when my results come out in a slightly unexpected manner.

I don't mind it when my hypotheses don't match what actually happened in real life.

It means it really gets me thinking about geography and all the different reasons for why data might vary from what I was expecting.

It gives me lots to think about and lots to write about in my conclusion.

So, don't worry if sometimes your results go a little bit differently than you're expecting.