# Why is Statistics So Hard?

**It’s not. Not for the Average Appraiser Bear.**

In elementary school and high school level, what is taught are “the two types of statistics.” One is descriptive statistics, the other is inferential statistics.

**Descriptive statistics** are used to describe, to summarize, or characterize a data set, so the human mind can understand rows of numbers.

**Inferential statistics** were designed for the same reason, but to solve a data problem. Before computers and databases, you simply could not get all the data. You had to collect just part of it, and *infer* from the part to the whole. From the sample to the population. There were two ways to do this – judgment or randomness:

- Use your skill and keen judgment to collect some data (your comps);
- Carefully randomize your selection from the population (your random sample).

These statistics and confidence intervals tell how well a random sample explains a population.

Appraisers do not take random samples to pick comps. **No need for statistical inference**.

### If you have the data, you just use it.

Today. It’s. Simple. There is no need for inferential statistics. No need for confidence intervals, t-scores, Chi-squares, p-values, z-test, sample mean, sample standard deviation, or sample proportion. No need.

The *Appraisal of Real Estate* (Appraisal Institute, 14^{th}. ed., p.737): “Predictive models are predominant in most valuation settings.” Appraisal is a problem in prediction, not how to represent a population from a sample.

For our purpose, inferential statistics is essentially **obsolete**. It’s hard and a waste of time. It’s a curriculum teaching what is no longer needed — and never *was* needed for appraisers.

What we do need is simple. The only tools we really need are descriptives, such as mean, median, standard deviation, and coefficient of variation (COV – the normalized deviation measure). We have been led down a blind box canyon. Meanwhile, our competitors (such as AVMs, economists, accountants, and unlicensed evaluators) continue to provide the world of clients with what they need – an evaluation of risk for loans, investments, and legal damages. They use computers, modeling skills and subject-matter knowledge. This is data science

**Data science answers big data. **

We need to get out of this box canyon, and quickly. We need to find the trail to the ridge above, so we can see our competitors. See our clients’ needs: Risk evaluation. Intrinsic value. Sustainable value. Not over/under-exuberant market price pretending to be real value.

Inferential statistics is hard. Statistical inference is the wrong tool, the wrong model, the wrong problem.

Gary Kristensen

March 1, 2017 @ 8:08 am

Interesting, thank you for sharing. I look forward to future posts.