- Created by: GreenGooSnake
- Created on: 16-07-19 22:04
1) Scientists try to explain things. They start by observing something they don't understand.
2) Then they come up with a hypothesis - a possible explanation of what they've observed.
3) Then they test if it's right or not. They make a prediction based on it and by gathering evidence from investigations. If evidence from experiments backs up a prediction, you're closer to finding out if the hypothesis is true.
Testing A Hypothesis
Testing A Hypothesis.
1) Scientists share findings in peer-reviewed journals or at conferences.
2) Peer-review - Where other scientists check results and explanations to make sure they're 'scientific' before they're published. This helps to detect false claims, this doesn't mean that findings are correct - just not wrong in any obvious way.
3) Once other scientists have found out about a hypothesis, they'll base their own predictions on it and carry out their own experiments. Also they will try to reproduce the original experiments to check the results - and if all experiments back up the hypothesis, they think it's true.
4) If a scientist does an experiment that doesn't fit with the hypothesis then it may need to be modified or scrapped altogether.
1) Accepted hypotheses are often referred to as theories. Our currently accepted theories are the ones that have survived this 'trial by evidence' - they've been tested many times over the years and survived.
2) Theories never become fact. If new evidence comes along that can't be explained using the existing theory, then everything is likely to start all over again.
1) Representational model - a simplified description or picture of what's going on in real life. It can be used to explain observations and make predictions.
2) Computational models - Use computers to make simulations of complex real-life processes, such as climate change. Used when there are lots of different variables (factors that change) to consider, and because you can easily change their design to take into account new data.
3) All models have limitations on what they can explain or predict. E.g. the big bang model doesn't explain the moments before the big bang.