Until now, we’ve assumed we know population parameters and . But often, we don’t. We only have samples from which to draw all our conclusions. In this lesson, you’ll learn a statistical method for concluding whether or not two populations are significantly different. There are two different cases where we can use this statistical test.

**Case 1**

We have a sample from a certain normally distributed population, and we want to decide if the population mean differs significantly from a certain value.

Examples

- A growing city is trying to increase tourism to boost its economy. Over the last five years, the average number of new tourists per day has been 378,369. A year after constructing new buildings and tourist attractions, urban planners took a sample of 30 random days throughout the year and found the average number of new visitors to be 432,765. Is this significantly different from the historic population mean of 378,369?
- This same city has a five-year goal of bringing an average of 500,000 new visitors per day to bring more money into the city and boost its economy. According to this sample, could they have reached their goal of 500,000 new tourists per day?

**Case 2**

We have two samples from the same population but under different conditions, and we want to decide if these two populations are significantly different.

Example

- Cell phone designers are deciding between two types of keyboards to use for a new phone they’re developing: the regular QWERTY keyboard, or one arranged in alphabetical order. They want to choose the keyboard that’s easiest to use, so they have a group of xx people type a certain sentence in each phone and record the number of mistakes they make on each. Is the difference significant enough to conclude that one keyboard is easier to use than the other?

Let’s go through Case 1 first. Our sample, with mean x̅, comes from a population with mean μ and standard deviation σ. We know x̅ and *s*, but not μ. We have to use our sample statistics to decide if μ is significantly different from a particular value (μ_{0}).

**This is a preview of Lesson 10. To access the full book, please purchase a hard copy or a digital version. If you opt for the digital version, you will receive a link via email within 1 business day.**

Continue to Lesson 11, or select a lesson below.

Lesson 1: Introduction to Statistical Research Methods

Lesson 2: Visualizing Data

Lesson 3: Central Tendency

Lesson 4: Variability

Lesson 5: Standardizing

Lesson 6: Normal Distribution

Lesson 7: Sampling Distributions

Lesson 8: Estimation

Lesson 9: Hypothesis Testing

Lesson 10: t-Tests for Dependent Samples

Lesson 11: t-Tests for Independent Samples

Lesson 12: Intro to One-Way ANOVA

Lesson 13: One-Way ANOVA: Test significance of differences

Lesson 14: Correlation

Lesson 15: Linear Regression

Lesson 16: Chi-Squared Tests

Afterward

Index