Review the key concepts, formulae, and examples before starting your quiz.
🔑Concepts
Evaluating evidence involves checking the reliability of data. Data is considered reliable if repeating the experiment yields similar results.
An anomalous result (or outlier) is a data point that does not fit the overall pattern or trend. These are often caused by human error or equipment malfunction and should be identified before calculating a mean.
A trend or pattern describes the relationship between variables. For example, 'As the temperature increases to , the rate of reaction also increases.'
To ensure a fair test, only the independent variable is changed. If other variables are not controlled, the evidence is not valid and cannot be used to draw a firm conclusion.
Comparing a prediction to the actual evidence: If the data matches the prediction, the hypothesis is supported. If not, the hypothesis may need to be modified or rejected.
The mean (average) is used to reduce the effect of random errors. The formula used is , where is the sum of values and is the count.
📐Formulae
💡Examples
Problem 1:
A student measures the distance a toy car travels in trials: , , and . Identify the anomalous result and calculate the mean of the reliable evidence.
Solution:
Anomalous result: . \nMean = .
Explanation:
The value is significantly different from the other two results, suggesting an error occurred during that trial. It is excluded from the mean calculation to ensure the final evidence is more accurate.
Problem 2:
In an experiment testing how temperature affects salt solubility, a student predicts: 'Higher temperatures will allow more salt to dissolve.' Their results show: , , . Does the evidence support the prediction?
Solution:
Yes, the evidence supports the prediction.
Explanation:
The data shows a clear positive trend. As the temperature increases from to , the mass of dissolved salt increases from to , which matches the student's prediction.