Course: Research Methods
Statistical tests rely on certain assumptions about the data to provide accurate results. If these assumptions aren’t met, the results may not be trustworthy.
An Introduction to Assumptions
When we use statistical tests, we make several assumptions about our data. These are like the rules of the game for statistics. If these rules aren’t followed, our game—meaning our research—might be off. If the assumptions of a statistical test are violated, the results may be unreliable or misleading.
Assumptions vary based on the type of statistical test. Some common assumptions include normality (data follows a normal distribution), homogeneity of variances (all groups have the same variance), and independence (observations are independent of each other).
Importance of Checking Assumptions
Why do we care about these? Well, they act as the foundation for our analysis. If our data doesn’t meet these assumptions, it’s like building a house on shaky ground. The house, in this case, is our conclusion. If the ground isn’t solid, the house could collapse. Similarly, if the assumptions aren’t met, our conclusions might not hold up.
When they are violated, our statistical tests might give us inaccurate results. This could lead us to make incorrect conclusions about our data. That’s why it’s essential to check them before running our tests.
A Real-world Example: Criminal Justice Research
Imagine a criminal justice researcher conducting a study on the effect of a new rehabilitation program on recidivism rates. They are using a statistical test that assumes the data follows a normal distribution. This means the data should spread out around the average in a specific bell-shaped pattern.
If the researcher doesn’t check this, they might miss that their data doesn’t follow this pattern. Maybe the recidivism rates are skewed towards lower rates, not spread evenly around the average. If they run their statistical test without checking this assumption, they might conclude that the rehabilitation program has a significant effect when it doesn’t.
By checking this of normality first, the researcher could decide to use a different statistical test better suited to their data. This helps ensure their conclusions about the rehabilitation program’s effectiveness are more reliable and accurate.
Conclusion
Assumptions of statistical tests are not just abstract, complicated concepts. They are crucial steps in ensuring the reliability and validity of research findings. For social researchers, understanding these can mean the difference between accurate insights and misleading conclusions.