Conduct an experiment where you cannot predict the result.
You could roll a dice with a piece of 'Blu Tack' stuck to it.
Or make a spinner where the axis is not quite in the centre.
Or drop a drawing pin.
Conduct the experiment many times and work out the relative
frequency of a 'success' after every 10 or 20 trials.
Plot a relative frequency graph like the one above to see if the
results 'settle down' to a consistent valu

To conduct an experiment where the outcome is unpredictable, we can design a trial and then perform it multiple times, tracking the relative frequency of a "success" after every 10 or 20 trials. Here’s an outline for each suggested experiment:

### 1. **Rolling a Dice with Blu Tack Attached:**
   - **Setup:** Attach a small piece of Blu Tack to one side of a six-sided die. This could bias the die, making certain sides more or less likely to land face up.
   - **Success Criteria:** Define a "success" as the die landing on a specific number (e.g., 6).
   - **Process:** Roll the modified die 100 times, recording whether each roll is a "success" or not. After every 10 trials, compute the relative frequency of "successes" (i.e., the proportion of trials that resulted in a success).
   - **Relative Frequency Formula:** 
     \[
     \text{Relative Frequency} = \frac{\text{Number of successes}}{\text{Total number of trials}}
     \]

### 2. **Off-Center Spinner:**
   - **Setup:** Create a circular spinner where the axis is not perfectly centered, causing uneven distribution of possible outcomes.
   - **Success Criteria:** Define a "success" as landing in a specific color or section of the spinner.
   - **Process:** Spin the modified spinner 100 times, and record the results. Compute the relative frequency of successes after every 10 spins.

### 3. **Dropping a Drawing Pin:**
   - **Setup:** Drop a standard drawing pin onto a flat surface.
   - **Success Criteria:** Define a "success" as the pin landing with the point facing upwards.
   - **Process:** Drop the pin 100 times, recording how often it lands point-up. Compute the relative frequency of "successes" after every 10 drops.

### **Plotting the Graph:**
Once you have the data, follow these steps to plot the relative frequency graph:
1. Record the relative frequency of success after each set of 10 trials.
2. Plot these points on a graph where the x-axis represents the number of trials (10, 20, 30, etc.) and the y-axis represents the relative frequency (ranging from 0 to 1).
3. As more trials are completed, the relative frequency graph may begin to stabilize and approach a consistent value.

Would you like me to simulate one of these experiments and generate the graph for you?

### 5 Related Questions:
1. How does the shape and position of the Blu Tack affect the bias in the dice?
2. How would shifting the center of the spinner's axis further change the outcome probabilities?
3. Can the results of the drawing pin experiment be predicted using physical principles (e.g., weight distribution)?
4. How does increasing the number of trials impact the stabilization of relative frequency?
5. What mathematical models can be used to predict the outcome of these biased experiments?

**Tip:** The Law of Large Numbers suggests that as the number of trials increases, the relative frequency of an event will stabilize around its true probability.

Conduct an experiment where you cannot predict the result. You could roll a dice with a piece of 'Blu Tack' stuck to it. Or make a spinner where the axis is not quite in the centre. Or drop a drawing pin. Conduct the experiment many times and work out the relative frequency of a 'success' after every 10 or 20 trials. Plot a relative frequency graph like the one above to see if the results 'settle down' to a consistent value.

Explore how to conduct experiments with unpredictable results, such as rolling a biased die, spinning an off-center spinner, or dropping a drawing pin. Track the relative frequency of 'success' after every 10 or 20 trials and plot a graph to see if results stabilize over time. Understand key mathematical concepts like probability, relative frequency, and the Law of Large Numbers. Suitable for students in Grades 7-10.

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