Battery Cell Quality Statistical Case

Data Engineer / Cell Integrity & Quality case: five randomly assigned battery groups each undergo three pass/fail tests; identify which group has the best overall quality and justify the statistical method. Requirements There are five groups of batteries. Batteries are randomly assigned to the five groups. Each battery goes through three different inspections or tests. Each test result is pass or fail. The data has already been stored. Determine which battery group has the best overall quality. Notes Treat this as an experiment-design and statistical-comparison case, not a pure SQL exercise. Clarify sample sizes, whether the three tests have equal importance, whether failures are independent, and whether a battery-level pass requires all three tests to pass. Reasonable approaches include group-level pass-rate estimates with confidence intervals, multiple-comparison-aware hypothesis testing, and a composite quality score if the tests have different severity. If each battery has three binary outcomes, define the unit of analysis before ranking: per-test pass rate, all-tests-pass battery rate, or weighted defect severity score can produce different winners. The prompt may require domain assumptions. State them explicitly before choosing a statistic. Preparation Build a small table with battery_id, group_id, and three pass/fail columns; compute group sample size, per-test pass rate, all-pass rate, and confidence intervals. Practice explaining how random assignment helps but does not remove the need to check group balance, missing inspections, and multiple comparisons across five groups. Prepare a recommendation format: rank by primary quality metric, show uncertainty bands, flag statistically indistinguishable groups, and list follow-up data needed before production action.