Hypothesis Testing: Definition, Steps & Examples

The research questions were formulated as:

Question 1: Are player’s earnings and rounds related to each other?

Question 2: Is there a relationship between the player’s age and driving accuracy?

Hypothesis 

The hypothesis for the first research question was formulated as:

H 0 : There is no correlation between a player’s earnings and rounds.

H 1 : There is a positive correlation between a player’s earnings and rounds.

The hypothesis for the second research question was formulated as:

H 0 : There is no correlation between a player’s age and driving accuracy.

H 1 : There is a positive correlation between a player’s age and driving accuracy.

Analysis Procedure and Results 

Descriptive Analysis 

A descriptive analysis was undertaken to analyze the numerical variables. The values of central tendency that were evaluated were the mean, median, and mode while the values of the variability that were evaluated were the range, standard deviation, minimum and maximum values. The results were as shown in table 1.

The numerical variables that were analyzed were summarized as shown in the table 1 and table 2.

Correlation Analysis 

A correlation analysis was performed between the variables of a player’s earnings and rounds.

Figure 1. Distribution Plots and Correlation Plot for Earnings and Rounds 

The diagrams showed that there was a positive relationship between the variables of earnings and rounds. However, the strength of the relationship was weak as indicated by the small value Pearson’s r of 0.334. The p-value of < .001 and a positive value in Pearson’s r meant that we reject the null hypothesis and we accept the alternative hypothesis. Players that have higher earnings have more rounds.

Correlation analysis was performed for the variables of age and driving accuracy. The results were as shown in figure 2.

Figure 2. Correlation plot and Correlation Analysis for Age and Driving Accuracy 

Analysis of the diagrams showed that there was a positive relationship between the variables of age and driving accuracy. However, the strength of the relationship was weak as indicated by the small value Pearson’s r of 0.341. The p-value of < .001 and a positive value in Pearson’s r meant that the null hypothesis is rejected and the alternative hypothesis is accepted. Players that have high driving accuracy are generally older.

Summary 

Are player’s earnings and rounds related to each other?

The means of the earnings and rounds were 1,646,000 and 71.3 respectively.

The standard deviations of earnings and rounds were 1,609,000 and 21.604 respectively.

A correlation analysis was performed.

Result: There was a statistically significant positive relation between earnings and rounds (r = 0.334, p < .001, α = .05). Thus reject the H0 and accept H1.

Conclusion: Players that have higher earnings are more likely to have more rounds.

Is there a relationship between the player’s age and driving accuracy?

The means of the age and driving accuracy were 33.395 and 60.824 respectively

The standard deviation of age and driving accuracy were 6.679 and 5.104 respectively.

A correlation analysis was performed.

Result: There was a statistically significant positive relation between earnings and rounds (r = 0.341, p < .001, α = .05). Thus reject the H0 and accept H1.

Conclusion: Players that have high driving accuracy are generally older.