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Meaning: Proportion of times that an event occurs in the long run when the conditions are stable, or the observed relative frequency of an event in a very large number of trials. This method uses the relative frequencies of past occurrences as probabilities. We determine how often something has happened in the past and use that fi8gure to predict the probability that it will happen again in the future. For example, suppose that an accounts receivable manager knows from past data that about 70 of 1000 accounts usually become uncollectible after 120 days. The manager would estimate the probability of bad debts as 70/1000 = .07 or 7%. A second characteristic of probabilities established by the relative frequency of occurrence method can be shown by tossing one of our fair coins 300 times. Here we can see that although the proportion of heads was far from 0.5 in the first 100 tosses, it seemed to stabilize and approach 0.5 as the number of tosses increased. In statistical language, we would say that the relative frequency becomes stable as the number of tosses becomes large (if we are tossing the coin under uniform conditions). Thus when we use the relative frequency approach to establish probabilities, our probability figure will gain accuracy as we increase the number of observations. Of course, this improved accuracy is not free; although more tosses of our coin will produce a more accurate probability of heads occurring, we must bear the time and the cost of additional observations. One difficulty with the relative frequency approach is that people often use it without evaluating a sufficient number of outcomes. If you heard someone say, “My aunt and uncle got the flu this year, and they are both over 65, so everyone in that age bracket will probably get the flu,” you would know that your friend did not base his assumptions on enough evidence. His observations were insufficient data for establishing a relative frequency of occurrence probability.

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Q: What is relative frequency of occurrence?
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