Chemistry wouldn’t be a real science if it were completely theoretical or abstract. Chemistry will always be about the real world, making it a practical subject requiring practical skills. Just like other natural sciences, its findings are based on empirical evidence and experimentation.
Scientific method is the foundation of chemistry, and the practical skills you need to develop and master in chemistry GCSE are key to scientific investigations, especially when it comes to planning or designing your research, collecting data, analysing data, and evaluating or interpreting that data.
In this post:
Planning a Scientific Investigation
Identifying the problem
Any scientific investigation is based on a particular problem and premised with a hypothesis. A problem can be anything that is either unknown or needs to be confirmed. It doesn’t necessarily have to be something revolutionary; it can be as mundane and routine as testing the emission of a car based on the type of fuel used.
A hypothesis is simply a proposed explanation for what you’re about to investigate. A scientific hypothesis must provide a statement that can be tested, and acts as a prediction for what you expect to happen during the investigation.
Formulating a hypothesis
You cannot design or start a scientific investigation without first having a scientific hypothesis. After identifying a problem, you must formulate an explanation of the problem and make a prediction. You can then design an experiment or a methodical observation to test your hypothesis.
Compared to other observational sciences, like astronomy, you can have complete control of the variables of the phenomenon you want to investigate in chemistry. For example, you can devise a laboratory experiment that will measure and test the variables separately.
Knowing your variables
Isolating variables is useful to prevent confusion about the cause-and-effect correlation. It allows us to see exactly what each variable is doing so that we have a more accurate understanding of how it’s affecting the experiment.
You can also gradually increase the intensity of a variable to determine a threshold. A perfect example of this is a titration experiment, where a known concentration of a titrant solution can be gradually added to an analyte solution until a threshold value is reached, thereby determining the concentration of the analyte.
When planning a scientific investigation, you must focus on one specific factor or variable at a time. Variables are classified as dependent variables, independent variables, and control variables.
- Dependent variables: These are the variables that are being investigated. They usually change in response to the independent variables. Simply put, dependent variables are the effects. For example, if you’re doing a titration experiment, the change in colour of the analyte solution is the dependent variable.
- Independent variable: These variables are the ones you control to see what sort of effects they have on the dependent variables. Going back to the example of a titration experiment, the independent variable is the amount of titrant that you gradually add into the analyte solution to make it change colour. You can precisely measure the amount of titrant in milliliters. Another example of an independent variable is the temperature of a stove or bunsen burner that you can gradually increase to determine the boiling point of a liquid.
- Control variables: These variables are the ones that you keep constant to compare with the other variables. Control variables are important in calibrating an experiment. For example, if you’re testing the solubility of sodium chloride in water in relation to the temperature and concentration, you should keep the temperature of your control group at a constant while increasing the temperature in the dependent variable group.
Choosing Your Equipment and Methodology
After identifying the problem, formulating a hypothesis, and establishing your variables to be tested, you then need to design an experiment. You must include the correct instruments and measuring tools. It’s critical that your tools and instruments are appropriate and well-calibrated.
For example, if you want to determine the amount of potassium in bananas, you need to extract potassium metal from bananas. You then need to compare the weight of the bananas and the weight of the metal potassium that you will be extracting. Therefore, a very precise and correctly calibrated weighing scale is necessary for this experiment.
The more precise and accurate your measurements are, the more reliable your data will be. Your laboratory tools and instruments play an important role in gathering reliable data.
In some cases, you may need to use sophisticated instruments like mass spectrometers or a particle size analyser. On the other hand, if you’re aspiring to become a chemist, you’ll also need to learn sophisticated methods, like X-ray crystallography,to determine the structures of complex molecules like DNA.
Collecting the Data
Scientists collect data in several ways; through observation, measurements, and experimentations. Once you’ve identified your variables, you can set the parameters of your data. Common parameters include temperature, pressure, pH, colour change, mass, volume, and molar units.
You must meticulously observe these parameters and carry out precise measurements based on your experiments. Carefully record the data and organise it in tabular form. Be as quantitative in data collection as you can. You may also use qualitative data, such as change in colour or smell.
However, even qualitative data can be quantified to some extent. For example, in acidity-basicity tests using colour indicators, you can assign numerical values to colour change intensities by calibrating the colour changes using a pH chart if a pH meter is unavailable.
You can also plot your tabulated data on graphs. If the data is continuous, such as the correlation between temperature and solubility, you can use a scatter plot diagram. If the data is discrete and categorised, you can use a bar graph diagram. You may also use a pie chart if you want to visualise proportional relationships, such as the composition of mixtures.
Analysing the data
Data analysis isn’t easy if you don’t have the right mathematical tools or conceptual framework. In these cases, you may need the aid of computers to correctly analyse the data. Statistics are very helpful in analysing large volumes of data. Technically, all scientific data can be statistically analysed for validity and reliability.
In some cases, data analysis in chemistry will require creating molecular digital models to understand the complex reactions and processes involved, such as enzyme actions or the way proteins fold. If you’re analysing large and complex data, computer skills will be indispensable. You may also need knowledge in programming languages to customise software applications for your research.
Evaluating the data
Data evaluation requires objectivity and critical thinking. You must be able to identify random errors and systematic errors. As a scientist, you must not be emotionally attached to your research, or you will have a lot of blindspots.
The scientific process has the built-in means to check for possible errors and minimise biases. Peer reviews can also be used to weed out the errors.
- Random errors: These are errors that you don’t have control over. For example, even if you want to make the parameters constant or consistent, environmental factors may affect your experiments.
- Systematic errors: These refer to the errors caused by the wrong calibrations of equipment or errors due to the design of the research.
The practical skills that you’ll learn and master will help you in pursuit for a career in chemistry. These skills will be very useful if you want to be an effective researcher or scientist.
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