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So, welcome this morning.

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As you know, we're going to do
some discussion around data--

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what we think data
is, how we use data,

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and then looking
at the resources

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that we've all seen beforehand.

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I'd like you to start
thinking about how that makes

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you think about what
data is, and how

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it can be used in
society as well.

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Just to start the
conversation, can anyone

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give me an example of
where they use data?

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Prove an argument in an
essay, or in any way,

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really, just to
prove your argument.

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OK, so data to
prove an argument.

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Anyone else use data?

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I've just come form
an English lesson

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and we're looking at English
university league tables,

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so what data is into that.

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Fantastic example.

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Any other examples of data?

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Experiments.

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Experiments.

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We have a hypotheses.

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You can prove your hypotheses
with the data or disprove it.

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OK, so we've got
some key words there.

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We've got argument,
we've got proving,

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we've got ranking
to support argument.

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What do you see
data as being then?

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Facts and figures.

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Facts and figures.

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Anything else?

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Statistical evidence.

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Although nautical data would
be less the writer's opinion.

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It wouldn't be the
writer's opinion,

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it would be information
that he had derived

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from somewhere
else that was true,

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rather than opinion,
which is subjective.

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So for the second element, what
we're going to be doing now

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is relating what we were
discussing about data back

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to the A-Tech phone,
and the three

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paragraphs that we've had
to read prior to the session

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this morning.

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Can you just give me
an insight into what

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you thought the differences
between version one and version

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two was?

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There was far more statistical
evidence in the first one.

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A lot more percentages in
raw-- not raw, but not quite so

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manipulated data
or more numbers.

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Did you enjoy that?

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Did you enjoy just
having those facts there?

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I think you need a balance.

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Because the second one had
more interpretation of data,

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and I think that's
better for affecting

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people's actual opinions
towards the data.

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When it just said it was
20% lighter or something,

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it's hard to quantify
that in your head.

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So you do need an
explanatory paragraph

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or the point saying
explain it to you.

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Because it's just a
number, really-- you

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can't really understand what
that actually means to you,

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and how that affects it.

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So which one then encourages
you to ask more questions?

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Version two.

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Version two.

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Because it told you less.

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It glossed over
it, I think, a bit.

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I think it's about
putting it in a format

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that the general
public understands.

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If you're selling a
phone, for example,

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it would be easier to
understand in version two.

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Because it just says
faster, thinner, or lighter.

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That's easier to understand
than, as Nick said, 20%.

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So I think it's
about understanding,

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and version two is
easier to understand

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so it would sell better.

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What message do you
think the author

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was trying to convey
with version one?

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I thought it was trying to put
the facts out there, and then

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let people make a
judgement on their own

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rather than just saying
this is what I think of it,

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and I think you
should agree with me.

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It read more as a factual report
than a review on the item.

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How does version three
start to change the way

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that the data is
presented in these?

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They give you far more
data-- but some of it's

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completely irrelevant,
like the temperature

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on the day that they
were writing about.

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But they also give
you some opinions.

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So there's an element of
subjective opinion in there

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as well.

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I found it much
harder to understand.

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For example, they gave
the length of the phone

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in millimetres, which
is not a unit you

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would use in everyday life.

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You'd normally use centimetres.

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So I found it much harder
to make sense of the data,

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and so I couldn't really
picture the phone in my head.

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It wasn't selling
the product to me.

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So looking at the three
different versions, which

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one persuaded you more
about the product, and why?

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Version two because it made the
product clearest in your mind.

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You could imagine it.

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The data, it set
you up with a point,

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and then the data
confirmed that point to you

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so you thought it was true.

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But the first one you had to
make your own judgement of it.

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And so some of the judgments
I made, like 18% liked it,

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doesn't really matter.

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And if they say it's
lighter, it's better,

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you want us to believe
that it is better

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because it was lighter.

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So we've got version
two over there.

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Anyone else with version two?

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I though the version two
sold it to me better.

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But, for example, if I was
comparing between a few phones

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and I was deciding
which one to get,

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I would prefer
version one because I

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would be able to make
the best comparison.

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For example, if version two just
says it's lighter and faster,

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that's great, but how
much lighter or faster?

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For example, how much better
is it than the previous model?

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Is it worth me upgrading
to the new model?

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So for example, if
I was researching

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in buying the phone, version two
interested me in the product.

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But when I was
researching the phone

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I would probably
look to version one.

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So you'd go version two first?

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Yeah.

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And then version one to
almost support and check

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the accuracy of what version
two was trying to do?

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OK, so version two, version two.

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I agree that I'd use version two
first, and go onto version one,

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because version two
is also opinionated.

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And if you're trying to
make your own judgement,

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you want the facts without the
opinion so that you can compare

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it with another
supposedly neutral source.

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And finally.

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Version two would be
the one to persuade me

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into researching further.

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But then version
one will be the one

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I went back to to
check my facts.

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How do you think we'd use
data in a university context?

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I think at university
your work in research

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is a lot more in your own
hands, whereas at school it's

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sort of guided by teachers,
or if you have textbooks,

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it's in the textbook.

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But at university you
have to do a lot more

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of your own research.

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So I think it's
up to you to check

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that the data and
facts they're using

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are correct rather
than just believing

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that they are correct.

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You have to make sure that the
data you're using is reliable,

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or else the research that
you're doing won't be accurate.