How Poor Questions and Biased Samples Can Derail SVM Skill Test Results
Support Vector Machines (SVMs) are a powerful supervised machine learning algorithm used for classification and regression tasks. Data scientists and ML engineers should have a strong grasp of when to use SVMs, how to tune their hyperparameters, and interpret their outputs. Many employers and certification programs use skill tests, containing a set of multiple-choice questions (MCQs), to assess a candidate‘s knowledge of SVMs. However, poorly designed questions and biased samples of test takers can lead to misleading results that don‘t accurately reflect someone‘s SVM skills. Let‘s dive into the issues of poor questions and sampling bias and how to mitigate their impact on SVM skill tests.
The Pitfalls of Poor Questions
Writing effective multiple-choice questions is deceptively challenging. There are many ways MCQs can be flawed, leading to results that don‘t truly measure a test taker‘s knowledge of SVMs. Some common pitfalls include:
Ambiguous or unclear wording: Questions that are vaguely worded or use unfamiliar terminology can trip up even knowledgeable test takers. The wording of the question and answer choices should be as clear and specific as possible.
Multiple correct answers: Questions with more than one arguably "correct" answer diminish the validity of the question. The test taker may select a different answer than the one the question writer deemed correct.
Not actually testing relevant knowledge: Questions should assess knowledge that is actually important for working with SVMs in practice. Obscure trivia or overly-specific implementation details are not very meaningful. Good questions focus on key concepts, assumptions, use cases, and interpreting results.
Testing memorization over understanding: Well-designed questions test a deeper conceptual understanding, not just rote memorization of facts. They may require some analysis or application of knowledge to a novel scenario.
Flawed questions can lead to tests that don‘t accurately measure someone‘s genuine ability to work with SVMs. A skilled practitioner could get an artificially low score, while someone who is skilled at taking tests but lacks practical knowledge could perform misleadingly well.
Poor questions are also frequently too easy or too difficult. If most test takers get a question right or wrong, it is not very useful for distinguishing between skill levels. An effective question should have an appropriate level of difficulty and discriminate between test takers with stronger and weaker understanding of the topic.
The Risks of Biased Samples
Even if the questions on an SVM skill test are thoughtfully designed, biased sampling of test takers can still lead to skewed, unrepresentative results. Sampling bias occurs when some members of the target population are more or less likely to be included in the sample than others. Some types of sampling bias that could impact SVM skill test results include:
Volunteer bias: If test takers self-select to participate, the sample may over-represent people who feel more confident in their SVM skills or those with more free time to take an optional test. The results would not necessarily generalize to the broader population of SVM practitioners.
Demographic bias: If certain demographic groups, such as women or minority groups, are underrepresented in the test taker sample compared to their representation in the target population, the test results may not accurately reflect the skill level across all groups. The sample should match the demographic makeup of the population as closely as possible.
Access bias: Skill tests administered online may unintentionally exclude those without easy computer or internet access. This could lead to a sample that skews towards certain socioeconomic or geographic groups.
A biased sample can make it very difficult to draw accurate conclusions or comparisons from SVM skill test results. The sample may not be truly representative of the larger population of interest. Conclusions drawn from a biased sample, such as setting a passing score or comparing results across demographic groups, could be highly misleading.
Designing Better SVM Skill Tests
With the risks of poor questions and biased samples in mind, let‘s look at some best practices for creating more effective and equitable SVM skill tests:
Focus questions on key concepts and applications: Ruthlessly cut any questions that don‘t tie back to the core competencies required to work with SVMs. Ask yourself – is this testing something that really matters for the role? Nice-to-know trivia can be omitted in favor of need-to-know information.
Test for conceptual understanding: Move beyond simply asking test takers to regurgitate memorized facts. Test their ability to reason about SVMs and apply their knowledge to novel scenarios. For example, you could describe a situation and ask them to select the kernel trick that would be most appropriate, explain why, or interpret the results.
Pilot test your questions: Before deploying a question on a real skill test, have a sample of other subject matter experts review it and provide feedback. Pilot test the questions with a small initial group and check the results. Do the difficulty level and distribution of responses align with your expectations? Revise or cut questions as needed before scaling up.
Use item analysis: Item analysis involves calculating statistics about the performance of each individual question. This includes metrics like the proportion of test takers who answered it correctly, how well it discriminates between high and low performers on the overall test, and its correlation with other related questions. Use these insights to identify and eliminate or improve problematic questions.
Consider compensating participants: Paying participants, even a modest amount, can help combat volunteer bias and encourage a more diverse, representative group to take the test. This can be especially helpful when testing populations who may not otherwise have a strong incentive to participate.
Make the test accessible: Whenever possible, provide multiple avenues for taking the test, such as online and in-person options. Be mindful of the devices and internet connectivity participants will need. Offer accommodations for those with disabilities. Broadening the accessibility of the test helps combat access bias.
Compare participant demographics to the target population: Collect demographic information from participants and compare your sample‘s makeup to the demographics of the larger group you want to generalize the results to. If there are notable skews, consider targeted outreach to underrepresented groups. You may need to caveat your findings if you aren‘t able to achieve a representative sample.
Set an appropriate pass score: The passing score on the test should align with the level of SVM expertise needed for the role or certification. Avoid setting an arbitrary cutoff. Instead, use a method like the Angoff approach, where subject matter experts estimate the proportion of minimally-qualified candidates who would answer each question correctly.
Combine SVM skill tests with other evaluations: While a well-designed SVM skill test can provide very useful information, it should never be the sole determinant of someone‘s abilities. Skill tests are ultimately limited assessments, susceptible to cheating, test anxiety, and other issues. Use them in conjunction with interviews, work samples, and other holistic evaluations of a candidate or student‘s skills.
Conclusion
SVM skill tests can be a valuable tool for assessing someone‘s expertise with this machine learning algorithm when used effectively. However, poorly designed questions that are ambiguous, misleading, or not actually relevant to SVM mastery can lead to results that don‘t reflect the test taker‘s true abilities. Biased sampling of test takers, whether due to self-selection or lack of accessibility, also poses major risks to the validity of the results.
To combat these challenges, focus on writing questions that assess conceptual understanding of need-to-know SVM information. Pilot test the questions, use item analysis to evaluate their performance, and revise as needed. Take proactive steps to recruit a diverse, representative group of test takers and compare their demographics to the target population. Set a meaningful passing score and remember that skill tests should be combined with other skill assessments.
By putting in the effort to create thoughtful, well-validated SVM skill tests, we can more accurately identify those with genuine expertise and improve the overall reliability of these assessments. Recognizing the pitfalls of poor question design and sampling bias is the first step to overcoming them.
