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Single De Test

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During certain times of peak traffic, you may experience slower speeds. Throughput is very similar to bandwidth, in that it is a measurement of the amount of data that passes from one point to another within a certain amount of time.

This measurement is reflected as latency on a network. Not necessarily. There are many reasons why you may not be getting the results you expected from your speed test.

Once again, make sure you run the test multiple times to rule out any random dips in speed. Also be sure to double check that no one else is using their device on your network when you run the test.

Even a single smartphone can measurably alter your results if it is downloading a large update or streaming content at the same time as your speed test.

Our first recommendation is always to contact your service provider directly for help. They will be able to alert you to any network outages or other issues in your area, and can often troubleshoot your individual connection over the phone or via a live chat service.

If it does help, you may be experiencing signal issues with your WiFi. Enter a zip code to find providers in your area. Municipal Providers Municipal broadband in the US.

Why run an internet speed test? How to run a speed test Understanding your internet speed test results How much internet speed do I really need?

How does an internet speed test work? Speed test terms: how to understand your results Does a slow speed test mean I'm being throttled?

What to do if your speed test is much slower than expected. Speeds of 50 Mbps or more are suitable for: Heavy streaming or gaming households, 4K content Large families of power users Frequent large downloads.

For one, it could potentially save you some money. How To Run A Speed Test Before you start the test, there are a few things you can do to ensure you get the most accurate results.

Be sure to place the laptop, tablet or smartphone you are using as close to your router as possible. Distance, walls, plumbing and other factors can all have an effect on your results.

Be sure to turn off any other devices that might be clogging your connection. Understanding Your Internet Speed Test Results In order to better understand your internet speed test, it is vital to know the difference between upload speed and download speed.

Upload Speed Upload speed refers to how quickly your connection can send something data, in this case from your device to the wider internet.

Download Speed Download speed refers to how quickly your connection can retrieve data from a website or server online.

For reference, there are 1, bits in a single Kilobit Kb. There are 1 million bits in a Megabit Mb. There are 1 billion bits in a Gigabit Gb.

Bytes A byte is a common unit of measurement for determining how large a particular piece of data is. For example, there are 1, bytes in a single Kilobyte KB.

Just like with the 1-sample t-test, for any given difference in the numerator, as you increase the noise value in the denominator, the t-value becomes smaller.

To determine that the groups are different, you need a t-value that is large. Each type of t-test uses a procedure to boil all of your sample data down to one value, the t-value.

The calculations compare your sample mean s to the null hypothesis and incorporates both the sample size and the variability in the data.

A t-value of 0 indicates that the sample results exactly equal the null hypothesis. In statistics, we call the difference between the sample estimate and the null hypothesis the effect size.

As this difference increases, the absolute value of the t-value increases. From the discussion above, we know that a t-value of 2 indicates that the observed difference is twice the size of the variability in your data.

However, we use t-tests to evaluate hypotheses rather than just figuring out the signal-to-noise ratio. We want to determine whether the effect size is statistically significant.

To see how we get from t-values to assessing hypotheses and determining statistical significance, read the other post in this series, Understanding t-Tests: t-values and t-distributions.

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Search for a blog post:. Subscribe Minitab. How 1-Sample t-Tests Calculate t-Values Understanding this process is crucial to understanding how t-tests work.

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A SPECT scan is a type of nuclear imaging test, which means it uses a radioactive substance and a special camera to create 3-D pictures.

While imaging tests such as X-rays can show what the structures inside your body look like, a SPECT scan produces images that show how your organs work.

For instance, a SPECT scan can show how blood flows to your heart or what areas of your brain are more active or less active. The most common uses of SPECT are to help diagnose or monitor brain disorders, heart problems and bone disorders.

SPECT can be helpful in determining which parts of the brain are being affected by:. Areas of bone healing or cancer progression usually light up on SPECT scans, so this type of test is being used more frequently to help diagnose hidden bone fractures.

SPECT scans can also diagnose and track the progression of cancer that has spread to the bones. If you receive an injection or infusion of radioactive tracer, you may experience:.

SPECT scans aren't safe for women who are pregnant or breast-feeding because the radioactive tracer may be passed to the developing fetus or the nursing baby.

Your health care team uses a small amount of radiation in order to perform a SPECT scan, and the test is not associated with any long-term health risks.

Then the SPECT machine rotates around you, taking pictures of internal organs and other structures highlighted by the radioactive tracer in your body.

You'll receive a radioactive substance through an intravenous IV infusion into a vein in your arm.

The tracer dose is very small. You may feel a cold sensation as it enters your body. You may be asked to lie quietly in a room for 20 minutes or more before your scan while your body absorbs the radioactive tracer.

In some cases, you may need to wait several hours or, rarely, several days between the injection and your SPECT scan.

A common analogy is that the t-value is the signal-to-noise ratio. The numerator is the signal. You simply take the sample mean and subtract the null hypothesis value.

If your sample mean is 10 and the null hypothesis is 6, the difference, or signal, is 4. If there is no difference between the sample mean and null value, the signal in the numerator, as well as the value of the entire ratio, equals zero.

For instance, if your sample mean is 6 and the null value is 6, the difference is zero. As the difference between the sample mean and the null hypothesis mean increases in either the positive or negative direction, the strength of the signal increases.

The denominator is the noise. The equation in the denominator is a measure of variability known as the standard error of the mean.

This statistic indicates how accurately your sample estimates the mean of the population. A larger number indicates that your sample estimate is less precise because it has more random error.

We include the noise factor in the denominator because we must determine whether the signal is large enough to stand out from it. Both the signal and noise values are in the units of your data.

If your signal is 6 and the noise is 2, your t-value is 3. This t-value indicates that the difference is 3 times the size of the standard error.

However, if there is a difference of the same size but your data have more variability 6 , your t-value is only 1. The signal is at the same scale as the noise.

In this manner, t-values allow you to see how distinguishable your signal is from the noise.

Relatively large signals and low levels of noise produce larger t-values. Many people are confused about when to use a paired t-test and how it works.

The paired t-test and the 1-sample t-test are actually the same test in disguise! As we saw above, a 1-sample t-test compares one sample mean to a null hypothesis value.

A paired t-test simply calculates the difference between paired observations e. You can test this with this data set to see how all of the results are identical, including the mean difference, t-value, p-value, and confidence interval of the difference.

Understanding that the paired t-test simply performs a 1-sample t-test on the paired differences can really help you understand how the paired t-test works and when to use it.

You just need to figure out whether it makes sense to calculate the difference between each pair of observations. If the before and after scores in each row of the example worksheet represent the same subject, it makes sense to calculate the difference between the scores in this fashion—the paired t-test is appropriate.

Using the paired t-test simply saves you the step of having to calculate the differences before performing the t-test.

You just need to be sure that the paired differences make sense! When it is appropriate to use a paired t-test, it can be more powerful than a 2-sample t-test.

For more information, go to Overview for paired t. The 2-sample t-test takes your sample data from two groups and boils it down to the t-value.

The process is very similar to the 1-sample t-test, and you can still use the analogy of the signal-to-noise ratio. Unlike the paired t-test, the 2-sample t-test requires independent groups for each sample.

For the 2-sample t-test, the numerator is again the signal, which is the difference between the means of the two samples. For example, if the mean of group 1 is 10, and the mean of group 2 is 4, the difference is 6.

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