When you cannot safely assume normality, you can perform a nonparametric test that doesn’t assume normality.įor the two-sample t-test, we need two variables. The second variable is the measurement of interest. We have students who speak English as their first language and students who do not.We also have an idea, or hypothesis, that the means of the underlying populations for the two groups are different. Our two groups are the native English speakers and the non-native speakers. Our idea is that the mean test scores for the underlying populations of native and non-native English speakers are not the same. We measure the grams of protein in two different brands of energy bars.We want to know if the mean score for the population of native English speakers is different from the people who learned English as a second language. Our measurement is the grams of protein for each energy bar. Our idea is that the mean grams of protein for the underlying populations for the two brands may be different. We want to know if we have evidence that the mean grams of protein for the two brands of energy bars is different or not. The variances for the two independent groups are equal.įor very small groups of data, it can be hard to test these requirements.Data in each group are normally distributed.Data in each group must be obtained via a random sample from the population.Measurements for one observation do not affect measurements for any other observation. However, since trace elements were relatively uniformly distributed in the soil profile, their concentrations were also possibly controlled by plowing and/or pedogenic processes.Below, we'll discuss how to check the requirements using software and what to do when a requirement isn’t met. Organic matter and clay content could be important parameters in controlling trace element concentrations and distribution in this study. The Mn, Ba, and Zn concentrations were probably elevated from the usage of fertilizers. The trace element concentrations found in this study are lower than levels established by the US environmental agencies and are therefore not considered dangerous. Similarly, Mn showed little association and no statistical significance to organic matter, whereas the rest of trace elements exhibited weak association and highly significant correlation. Statistically, Mn showed moderately significant correlation to Co and Cu, whereas the rest of trace elements displayed highly significant correlation each other. The Co, Pb, Sr, and Cr concentrations did not change with depth, the Zn and Ba concentrations decreased with depth, and the Mn and Cu concentrations increased with depth. The concentration of Mn, Ba, and Zn accounted for more than 82% of all the trace elements in the samples. Statistical analysis of the trace element concentrations, sand, silt, clay, fraction, and the percentage of organic matter were done using MINITAB 15.0 Statistical Software. The soil samples were prepared for trace element analysis on an ICP-OES following EPA method 3051 A. Organic matter analysis was conducted using a 3 % H 2O 2 solution. The grain size distributions in the soils were analyzed using a hydrometer. The aims of this study were to: 1) establish the concentrations of Ba, Co, Cr, Cu, Mn, Pb, Sr, and Zn in arable soils in Wood County, Ohio, 2) determine if the fractions of sand, silt and organic matter and/or soil depth were related to the distribution of these trace elements, and 3) help establish trace element background concentrations in Ohio.įifteen soil samples were collected at five depths using 10 cm interval from three locations within the former agriculture land.
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