BIAS IN DATA 1. By the sample population 2. The way questions are asked 3. By misleading graphs 4. By the use of inappropriate statistic(s) 1. By the sample group There must always be an element of randomness about the selection. 1. The Sample Population 2. By the way the question is asked A survey question is biased if it is phrased or formatted in a way that skews people towards a certain answer or if the question already assumes something to be true that maybe is not. a) How great is our hard-working customer service team? b) How awesome is the product? c) What problems do you have with the design team? a) How would you describe your experience with the customer service team? b) How would you rate this product? c) How likely are you to recommend working with the design team? 2. By the way the question is asked MISLEADING GRAPHS https://venngage.com/blog/misleading-graphs/ https://venngage.com/blog/misleading-graphs/ 10 learners are asked what is their weekly pocket money they receive at home. The following responses were recorded (already ordered): R20, R20, R25, R30, R40, R45, R50, R50, R50, R200 Q1/Lower Q Median/Q2 Q3/Upper Q Which of these statistics does NOT represent the set very well: 1) Mean = R53/week more than 90% of data because of the outlier 2) 3) 4) 5) Median = R42,50/week Mode = R50/week more than 60% of data Range = R180 because of the outlier IQR = R25 the middle 50% of the data has a range of R25 and excludes the outlier In summary: Data and Statistics can mislead you by .. • The sample population is NOT representative/random • The questions/observations to gather the information are BIAS • Using a title that suggests something different than the data • Leaving off the labels or scale • Using a very small range of numbers on the scale to make differences look greater • Using a very large range of numbers on the scale to minimize differences • Starting the scale of a bar graph at a number other than zero • Using a scale with uneven increments • Using a 3-D design to make it harder to compare quantities • Look for outliers that affect the averages or range.
