Introduction:
Statistics is the study of collecting, analyzing and interpreting data. It is used to make decisions based on the information collected. However, statistical analysis can be affected by the multiplicity of causes.

Explanation:
Multiplicity of causes refers to the fact that there are often many factors that can contribute to a particular outcome. For example, if we want to investigate the factors that contribute to obesity, we might consider factors such as diet, exercise, genetics, and environment.

Effects of Multiplicity of Causes on Statistics:
The multiplicity of causes can have several effects on statistical analysis. Some of them include:

1. Increased complexity: When there are multiple causes, it becomes more difficult to determine which factors are most important. This increases the complexity of the statistical analysis.

2. Increased variability: Multiple causes can increase the variability of the data, making it more difficult to draw accurate conclusions.

3. Confounding variables: Confounding variables are factors that can affect the outcome of the study but are not being measured or controlled. When there are multiple causes, it can be difficult to account for all the confounding variables, which can lead to inaccurate results.

4. Interaction effects: Interaction effects occur when the effects of two or more causes are not simply additive. This can make it difficult to predict the outcome of a study based on the individual effects of each cause.

Example:
Suppose we want to investigate the factors that contribute to a student's academic performance. Some of the factors that can contribute to academic performance include:

1. Studying habits
2. Attendance
3. Home environment
4. Teacher quality
5. Peer influence

If we tried to investigate each of these factors individually, we might not get a complete picture of what contributes to academic performance. For example, a student with good studying habits might still struggle academically if they have poor attendance or a distracting home environment. Similarly, a student with a great teacher might still struggle academically if they are influenced by negative peers.

Conclusion:
In conclusion, the multiplicity of causes can have a significant impact on statistical analysis. It is important to consider all the factors that can contribute to a particular outcome and to account for any confounding variables. This can help to ensure that the statistical analysis is accurate and reliable.