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Why Join This Lesson?
- 📚 Build a robust English vocabulary.
- 🧠 Develop critical thinking skills in English.
- 🗣️ Improve conversation and listening abilities.
- 🌐 Gain insights into real-world scientific scenarios.
- ✅ Perfect for learners at all levels: beginner, intermediate, advanced.
Extraordinary claims require extraordinary evidence.
⭐ Carl Sagan
In this lesson, you're not just boosting your English listening skills. You're also sharpening your critical thinking in English. As you delve into the nuances of scientific research, you learn to question and analyse information.
This skill is vital in today's world, where we're constantly bombarded with data. By engaging with this content, you're training your brain to think deeply in English, enhancing your fluency and understanding. Keep listening, keep questioning, and watch your English skills soar!
Not everything that can be counted counts, and not everything that counts can be counted.
⭐ Albert Einstein
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Welcome to an exciting English lesson where we dive into the fascinating world of scientific research! Discover how reliable science really is while improving your English skills.
Science and everyday life cannot and should not be separated.
⭐ Rosalind Franklin
Enhance your understanding of scientific research in English:
- Enhances listening skills in English through engaging content.
- Teaches vocabulary in context, like 'research', 'biased', 'falsify'.
- Builds analytical skills in English using real-world examples.
- Improves comprehension of complex topics in English.
- Encourages critical thinking through analysis of research validity.
- Introduces specific pronunciation of key words.
- Offers insight into common English phrases and idioms.
- Provides exposure to British English accents and intonations.
- Demonstrates use of English in discussing scientific concepts.
- Helps identify reliable vs. misleading information in English.
Each of these aspects contributes to your journey towards fluency in English, especially through listening and understanding complex topics.
The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.
⭐ Stephen Hawking
This lesson is not just about learning English. You'll sharpen your critical thinking, understand complex topics easily, and confidently analyse information. Plus, it's a great way to increase your language proficiency!
- Understand the reliability of scientific research.
- Learn to distinguish between valid and misleading data.
- Enhance English skills by focusing on common words and phrases.
- Develop critical thinking skills in English.
- Gain insights into British culture and thinking.
In today's world, we're constantly surrounded by information. This lesson helps you navigate through this data-heavy environment by teaching you how to question and analyse information critically. It's a vital skill, not just for understanding science but for everyday life. Plus, you'll get a peek into British culture and improve your English fluency!
Ready to boost your English and become a savvy researcher? Join our 'Listen & Learn' podcast for practical, everyday English use and become a part of our learning community. Let's embark on this journey together!
Exploring the reliability of scientific research in this British English lesson is like navigating a complex maze with a torch of truth, illuminating paths of understanding amidst shadows of doubt.
- How can I tell good research from bad in scientific studies? To distinguish good research from bad, look for signs of falsified or biased data. Falsified data means the information is made up, often for financial gain. Biased data may be based on truth but presented misleadingly. Always check if the research is peer-reviewed, published in reputable journals, and if the results are replicated in other studies.
- What is an example of falsified data in scientific research? An infamous example is the 2014 scandal involving car companies falsifying diesel emissions data. They manipulated results to appear more environmentally friendly than they were. Such instances demonstrate the importance of critical evaluation of research claims, especially when financial interests are involved.
- What does "biased" data mean, and how does it affect research? Biased data means presenting information in a way that misleads or supports a certain viewpoint. It can occur intentionally or unintentionally. For example, a pharmaceutical study might omit participants with severe side effects, giving a skewed view of a drug's safety. It's crucial to consider potential biases in research to understand its true implications.
- Why is it important to understand 'absolute' vs. 'relative' risk in research studies? Understanding the difference between absolute and relative risk helps interpret research findings accurately. For instance, a drug might reduce disease risk by 50% (relative risk), but in absolute terms, this might mean a reduction from 2% to 1%. Knowing both perspectives helps you make informed decisions about the actual benefits of a treatment or intervention.
- How can I improve my English while learning about scientific research? Listen to podcasts and read articles discussing scientific research. Pay attention to specific vocabulary like 'falsify', 'bias', and 'correlation'. Engage in discussions or write summaries to practice using new terms. This approach not only builds your English skills but also enhances your critical thinking and analytical abilities in the language.
- Falsify: To create fake information or data.
- Biased: Showing an unfair preference for or against something.
- Misleading: Giving the wrong idea or impression.
- Unintended: Not planned or meant.
- Correlation: A connection or link between two things.
- Causality: The relationship between cause and effect.
- Agenda: A plan or goal, especially in politics or business.
- Replicate: To copy or repeat something.
- Quantify: To measure or express the size or amount of something.
- Psychotherapy: A treatment for mental health problems that involves talking to a therapist.
Hi there. In today's podcast, we're looking at truth in scientific studies and why research does not always reflect reality. Not all scientific research is as valid as it sounds. Let's uncover the truth together today, while improving your English listening and enhancing your analytical skills in English too.
Health news is a frequent topic in the podcast. So I often quote what I call 'scientific studies' or 'research', R E S E A R C H. But how reliable is this data? What can go wrong with research? Reliable, R E L I A B L E, means 'you can rely on it', means 'you can rely on it to be true'.
These are great questions and the subject of valid research in its own right. So stay tuned - lots of ways to tell whether the research you're reading is valid or not.
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So, today's question, how do you tell good research from bad? What are the ways in which research can go wrong?
The worst is, of course, falsified data. ' To falsify', F A L S I F Y, means 'to make it up'. And there are examples of people or organisations who have 'falsified' data. Usually where this happens, there is financial gain. People make money out of it.
An example of 'falsified data' that you may remember? It was in the news in 2014 when various car companies, car makers, were cheating, 'falsifying' the data on diesel emissions for their cars, making the results look much better than they really were. That's an example of 'falsified data'.
A classic laboratory scene with beakers, microscopes, and scientific equations on a chalkboard. Boost English and analytical abilities.
Completely falsified data isn't that common. But what is much more common is what we call in English 'biased' data. That's B I A S E D, 'biased'. Meaning 'it's based on the truth, but presented in a way that misleads us'. So that it appears to mean something that it doesn't. Just imagine if this happened on a pharmaceutical drug trial. You do a 'pre-study' for a new drug, a 'pre-study' for a new drug trial. So that means 'a study before the main study'. And let's say that on that 'pre-trial', the people with the really bad side-effects from the drug drop out. They don't want to carry on and be part of the main trial. So you remove them. The bad side-effects that they had aren't counted and you're left with the people, the trial participants, who don't get such bad side-effects from the drug.
You could then say 'Our new drug is really well tolerated. So few people had side-effects!' That wouldn't be right, would it, if anybody did that? It would be 'misleading'.
Another thing which is very common practice, and again, I think is misleading, especially in terms of the benefit of a drug or medicine - it's presented in 'relative', not 'absolute', terms. I'll explain.
If I told you, 'Ooh, this drug will cut your risk of this particular disease by 50 per cent, you'd probably want to take the drug, wouldn't you? It would seem worthwhile. But that's a 'relative' figure. If I gave you the 'absolute' figure, the 'absolute' risk reduction of this drug, maybe it would cut your risk from 2 percent of getting the disease to 1 per cent. Would you think it was worth taking then? It's still a 50 percent reduction, but it's not the same, is it? That's misleading. Always ask your doctor for the 'absolute' risk reduction figures for a drug rather than the 'relative'.
Then there's a whole area of research, which ends up being misleading unintentionally, without someone having that purpose. ' To mislead', M I S L E A D, and the past participle 'misled', M I S L E D - verb 'to mislead' means 'cause you to draw a conclusion that's not true or not quite true'. ' There's also the adjective 'misleading', M I S L E A D I N G.
So, an example of unintentional 'misleading' data. Early in the 2000s, the Labour government in the UK decided to promote the use of diesel cars above petrol engine cars.
' Better for the environment!', they said.
They gave incentives for drivers to buy diesel rather than petrol cars.
What happened when there were more diesel cars on the roads? Well, it's estimated that there were many thousands more deaths from lung disease because of the greater number of diesel cars. And the effect of diesel fumes on people's health.
The research hadn't looked at this particular effect, only at the idea that diesel cars create less carbon dioxide, less CO2, and that was better for the environment. So that was a disaster. It's what we call 'unintended consequences' - an outcome that you didn't see or expect.
And this happened because the research didn't ask all the right questions. It only looked at the effect of diesel cars on the environment, not on people.
Sometimes the way that research is reported in the press, in the newspapers and the TV news - that's also misleading. They're of course looking for a story, so the more sensational way of talking about the data is what they choose. Again, it's misleading because a particular group have an investment or a particular interest in something - here selling newspapers. They're 'biased', in other words.
Complex questions like 'Is breastfeeding linked to higher intelligence?' lead to unrealiable answers?
And sometimes research is misleading because there are just so many factors at work, not all of which are particularly measurable, or they may not all be considered in the study.
For example, you might ask a research question like 'Is there a connection between breastfeeding and intelligence - t he number of breastfed babies who get good exam results later on when they take their GCSEs, let's say? Is there a connection between the number of babies who are breastfed and the number of those babies who get good GCSE exam results when they're 16?
Breastfed, B R E A S T F E D, is the past participle of the verb 'to breastfeed'. And if you're 'breastfed', it means 'your mother fed you herself with her own milk, naturally'.
I'm sure if you collected this data, you would probably find a connection between breastfeeding and better exam results. Or certainly a 'correlation', that's C O R R E L A T I O N, meaning 'some kind of relationship between these two pieces of data'.
So you could think on reading this that people are more intelligent because they were breastfed. But is this correct?
I think the difficulty with research of this kind is that there are so many other factors at work, it's hard to tell.
Breastfeeding is quite difficult and it's more likely to happen in situations where mothers are highly motivated, educated, well-supported, better-off. It's more likely to happen where there is already 'advantage'. That's in the UK at least. So those factors of 'advantage' are also likely to contribute to whether or not a child does well at their exams, when they're 16 years old.
So 'correlation is not causality'. It doesn't mean that one thing causes the other.
Something else which can affect research results. There may be political or organisational investment in a particular outcome from the research, a particular result. Meaning that an organisation or 'politics in general', wants the results to look a certain way. And this can have the effect of biasing the data.
In some areas, this isn't so much of a problem. But in other areas, the political agenda, that's A G E N D A - here meaning 'what people want to happen' - the political agenda can cloud results.
I think this is increasingly a threat in areas like Psychology. In extreme cases, if the person doing the research has findings, has results, which go against the usual way of thinking - they might be 'de-funded'! They may not get any more money for research, or it may damage their reputation.
Some people have staked their careers on research results that say a certain thing. If someone comes up with data that contradicts this, it's not always popular!
But true scientific inquiry should always be driven by the results, by the authentic results, even if that goes against what's gone before. This can be made really difficult by funding and circumstances.
Another problem, where the results of research are measurable and there are a few variables, scientific testing is very dependable. This is where science and research are at their best. But there are many subject areas, where things are not very measurable, not easy to quantify. That's Q U A N T I F Y. ' To quantify' means 'to measure the amount of something'. One of these is in the field of mental health, my specialism. Human beings are complicated and there are lots of factors, 'multiple variables', as we say.
I think one of the problems is that Psychology tries to be a science in order to be credible, believable in other words, but it's dealing in something - human minds - where science matters, but individuals are all different. And the understanding of human minds is as much an art, a skill set, as a science. That's why I prefer Psychotherapy as a discipline. It's allowed to be more of an art!
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I'll talk to you at some point in the future about the 'Psychology Replication Crisis'. The crisis in Psychology at the moment where they're trying to replicate previous experiments, which the field depends upon, which Psychology students learn. They've tried to replicate famous experiments and largely been unable to. That's another podcast for another day.
So when I quote 'research' or 'scientific studies' to you in podcasts, I try to make sure that my sources are good. But I'm also aware that many things that are probably true aren't very measurable or provable with research and probably no one's going to fund that research anyway. But it doesn't mean they're not true!
Hopefully that's given you some things to think about around the topic of scientific research. Let us know what you think!
Enough for now. Have a lovely day. Speak to you again soon. Goodbye.
Thank you so much for listening. Please help me tell others about this podcast by reviewing or rating it. And, please share it on social media. You can find more listening lessons and a free English course at adeptenglish.com
- Diesel emissions scandal
- Government pushed 'dash for diesel'
- Is breastfeeding more for the middle class?
- Replication Crisis
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