First, consider the sample proportion, and then, examine the confidence interval.
The sample proportion is an estimate of the population proportion.Because the proportion is based on sample data and not on the entire population, it is unlikely that the sample proportion equals the population proportion. To better estimate the population proportion, use the confidence interval.
The confidence interval provides a range of likely values for the population proportion. For example, a 95% confidence level indicates that if you take 100 random samples from the population, you could expect approximately 95 of the samples to produce intervals that contain the population proportion. The confidence interval helps you assess the practical significance of your results. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation. If the interval is too wide to be useful, consider increasing your sample size. For more information, go to Ways to get a more precise confidence interval.
The interval plot shows the confidence interval for the sample proportion with a reference line that indicates the hypothesized proportion.
 

In these results, the estimate of the population proportion for households that made a purchase is 0.087. You can be 95% confident that the population proportion is between approximately 0.07 and 0.106.
 
 

In these results, the null hypothesis states that the proportion of households that bought a new product equals 6.5%. Because the pvalue is 0.0085, which is less than the significance level of 0.05, the decision is to reject the null hypothesis and conclude that the population proportion of households that bought the new product is different from 6.5%.