# What is non spurious in research?

## What is non spurious in research?

This is termed non-spuriousness, which simply means “not false.” A spurious or false relationship exists when what appears to be an association between the two variables is actually caused by a third extraneous variable.

## What does non spurious mean?

Non-spurious relationship — The relationship between X and Y cannot occur by chance alone. Eliminate alternate causes — There are no other intervening or unaccounted for variable that is responsible for the relationship between X and Y.

**What is Spuriousness in sociology?**

Key Takeaways. Spurious correlation, or spuriousness, occurs when two factors appear casually related to one another but are not. The appearance of a causal relationship is often due to similar movement on a chart that turns out to be coincidental or caused by a third “confounding” factor.

**What is an example of a spurious correlation is sociology?**

The oft-repeated example of a spurious relationship is when ice cream sales increase so do drownings. However, ice cream does not have a direct relationship to drownings, but instead warmer weather drives increased ice cream sales and the amount of people who go swimming, thus increasing the number of people drowning.

### How can you reduce Spuriousness in research?

The best way to eliminate spuriousness in a research study is to control for it, in a statistical sense, from the start. This involves carefully accounting for all variables that might impact the findings and including them in your statistical model to control their impact on the dependent variable.

### Why are sociologists concerned with spurious relationships?

Every sociology major learns about the concept of spurious correlation, but they don’t always fully understand it. This concept matters because when it occurs, two things look like they cause each other, but in reality they don’t.

**What does spurious really mean in terms of research?**

Spurious is a term used to describe a statistical relationship between two variables that would, at first glance, appear to be causally related, but upon closer examination, only appear so by coincidence or due to the role of a third, intermediary variable.

**What are examples of spurious correlations?**

For example, ice cream sales and shark attacks correlate positively at a beach. As ice cream sales increase, there are more shark attacks. However, common sense tells us that ice cream sales do not cause shark attacks. Hence, it’s a spurious correlation.

## How do non experimental research designs reduce the risk of Spuriousness?

How due non-experimental designs control for spuriousness? they use statistical controls. A variable is controlled when it is held constant so that the association between the independent and dependent variables can be assessed without being influenced by the control variable.

## Why is spurious correlation an important concept for researchers?

A spurious correlation can tell you about the relationships between different data in a sample. When statisticians analyze samples to test theories and hypotheses, they look for any cause-and-effect relationships between the variables they’re testing.

**What is an example of Spuriousness?**

Another example of a spurious relationship can be seen by examining a city’s ice cream sales. The sales might be highest when the rate of drownings in city swimming pools is highest. To allege that ice cream sales cause drowning, or vice versa, would be to imply a spurious relationship between the two.