Statistical Significance: Your Key to Drawing Conclusions from Samples

In the world of quantitative research, surveying entire populations is often too expensive. So, we rely on representative samples to understand broader trends. But how do we know if our sample accurately reflects the population? That's where statistical significance comes in.

What is Statistical Significance (Sig.)?

Statistical significance, often denoted as "sig." in statistical outputs, is your level of confidence [1]. It tells you the probability that the results you see in your sample are likely to be true for the entire population. Think of it as the risk you're willing to take of being wrong when you generalize your findings.

• If sig. <= 0.05, results can likely be generalized to the population. This is a common threshold in social sciences.

• If sig. > 0.05, refrain from making conclusions about the population and interpret results only at the sample level. Your findings might be specific to your sample and not reflect the larger group.

Example:

Let's say you survey 200 customers and find that 70% are satisfied with your product. Your statistical test gives you a significance value of 0.03. Because 0.03 is less than 0.05, you can confidently say that a similar percentage of all your customers (the population) are likely satisfied.

One-Tailed vs. Two-Tailed Hypotheses

Before interpreting your sig. value, consider the type of hypothesis you're testing.

• Two-Tailed Hypothesis: You're simply looking for a difference between groups (e.g., men and women have different satisfaction levels).

• One-Tailed Hypothesis: You're predicting a specific direction of difference (e.g., women have lower satisfaction than men).

For one-tailed hypotheses, divide your sig. value by 2 before comparing it to the 0.05 threshold.

Example:

You hypothesize that a new marketing campaign will increase sales. If your statistical test shows a sig. value of 0.04, you would divide it by 2, resulting in 0.02. Since 0.02 < 0.05, you can conclude that the campaign likely increased sales.