3Simple Random Sampling Every possible combination of sample units has an equal and independent chance of being selected.However…
4Systemic SamplingBeware coincidental bias of sample interval and natural area.RidgesRiver bendsEtc.
5Stratified Random Sampling The point is to reduce variability within strata.Example: if you were measuring average estrogen levels in humans, you would stratify male versus female.Can you think of some forest examples?
9Exercise in Random Sampling Student heights equals populationCalculate population mean, etc.Take a systemic 20% sample compare estimates of population.Take a 50% sample (systemic or random) and compare results.Calculate mean, variance, SD and CV of both population and samples.
10VariabilityThe differences between individuals or units in a population
11Standard Error of the mean Equals the standard deviation of all possible sample means around the true population mean.
12Finite Population Correction Factor The finite population correction factor serves to reduce the standard error when relatively large samples are drawn from finite populations
13Confidence Intervalspecify the precision of the sample mean in relation to the population mean.
16Effect of Standard Deviation The red distribution has a mean of 40 and a standard deviation of 5;the blue distribution has a mean of 60 and a standard deviation of 10.For the red distribution, 68% of the distribution is between 45 and 55;for the blue distribution, 68% is between 40 and 60.
17Sampling ErrorRather than work with absolute confidence limits, convert them to a percent of the sample mean which is called sampling error. The notation in the handbook is an upper case E. Take the confidence interval quantity and scale it to the sample mean by dividing by the sample mean. Express this value as a percent by multiplying by 100. By expressing the confidence interval as a percentage, the mean can be plus or minus the percentage derived.For example, at 95% confidence, an estimate of the mean has a confidence interval of 46.4 plus or minus 2.6. When expressed as a sampling error percent, the mean is plus or minus 5.6% which says the true population mean falls within 95% percent of the estimate.
18Determining Sample Size For a 95% confidence level, the t value approaches 2 as the sample size gets large, so a t value of 2 is commonly used when estimating sample size. The CV is the relative variability in the population being sampled. Use the population CV if known or use an estimate if it is not known. The E represents the desired sampling error, for example, 10%
19Items with Possible Impacts on Sampling Intensity
20Effect of CV ChangeAs the coefficient of variation increases, so does the required sample size.
21Using CV for Comparison Because CVs have no associated unit of measure, they can be useful in comparing sampling methods to determine which is most efficient.So which method of sampling would require fewer samples?
23Sample Selection – from Precruise data Determine the sampling error for the sale as a whole. (set to 10%)Subdivide (or stratify) the sale population into sampling components as needed to reduce the variability within the sampling strata.Calculate the coefficient of variation (CV) by stratum and a weighted CV over all strata. (this will be covered more later in the statistics lectures)Calculate number of plots for the sale as a whole and then distribute by stratum.
24Number of PlotsValue of t is assumed to be 2Error is set at 10%
25Distribute Plots by Stratum For each stratum, the calculation would look like this:n1 = (17.6 * 185) / 67.9 = 48 plotsn2 = (7.7 * 185) / 67.9 = 21 plotsn3 = (7.2 * 185) / 67.9 = 20 plotsn4 = (35.4 * 185) / 67.9 = 96 plotsWhich totals to the 185 plots for the sale.
26Tree Expansion Factor1 divided by the fixed plot size times the number of plots
27Sample Error - step 1 (Calculate Standard Error)
28Sample Error – Step 236.2% is a bit larger than the level we set to begin with (10%) – Implications?