Statistics

Statistics

Many different types of statistics are used when conducting a population analysis.

Normal distribution will be use to show the outliers in the data, since normal distribution shows what 'normal' data points would look like. Standard deviation can be used hand in hand with normal distribution to measure how the data points are distributed; the higher the standard deviation, the more spread out the data points are. This means that if my data points were 1, 2, 3, 4, and 5, the standard deviation would be much smaller than if they were 1, 29, 39, 74, and 299. Continuing, variance measures how far each data point is from the mean of the data set; as a result, I will need to calculate the mean. Variance also shows how spread out the data is.

After all the manipulation of the data sets are complete, I will need to use these calculations to determine whether my null hypothesis or hypothesis is correct and how much error I have. In regards to testing my hypotheses, I will use a confidence interval, which will tell me when each of my two hypotheses are supported. Then I will use a t-test to determine if my null hypothesis is correct or not. P-values and z-values will be used in order to test my hypothesis and see when the null hypothesis should be taken as correct. Margin of error will tell me how accurate my conclusions are.

I will measure the standard error in my data to see if there are any gaps between the population and the sample size. Standard error can be up to double whatever I calculate it to be. Experimental error is important to take into consideration and can include human error, like counting a crab twice or not counting a crab at all. This will, obviously, affect my data.

All of these different types of statistics will help me to determine which hypothesis is correct and how to accurately read and interpret the numbers.